The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index; 32 of the 39 sources its claims cite were checked for support.
High-performance brain-to-text communication via handwriting
Open accessPublished version of a preprint
Abstract
Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping 1-5 or point-and-click typing with a computer cursor 6,7. However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute)8. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.
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Claims the paper makes
20 Own result · 11 Own interpretation · 7 Supported · 7 Partly supported · 3 No basis given · 2 No full text of the sources · 1 Supported so far · 1 Not supported · 1 Own method · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“During parameter optimization, the cursor gain is increased so as to increase typing rate, until the cursor moves so quickly that it becomes uncontrollable due to decoding errors22.”
§ High-performance brain-to-text communication via handwriting > Temporal variety improves decoding
What the sources say
- Partly supportedcites [22] Willett, F. R. et al. Signal-independent noise in intracortical brain–computer interfaces causes movement time properties inconsistent with Fitts’ law. J. Neural Eng. 14, 026010 (2017). sourceExperimental — accuracy not yet measured
The Results section, “Effect of gain on movement time” (page unavailable), reports that increasing gain initially shortened target-acquisition time, but higher gains made cursor movements fast, inaccurate, and uncontrollable around targets. The paper relates this difficulty to decoding noise, but it studies cursor target acquisition, not typing rate, and does not describe the claimed typing-speed optimization procedure. (LLM, conf=0.85)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The speed of point-and-click BCIs is limited primarily by decoding accuracy.”
§ High-performance brain-to-text communication via handwriting > Temporal variety improves decoding
What the sources say
- Partly supportedcites [22] Willett, F. R. et al. Signal-independent noise in intracortical brain–computer interfaces causes movement time properties inconsistent with Fitts’ law. J. Neural Eng. 14, 026010 (2017). sourceExperimental — accuracy not yet measured
The Results (Effect of gain on movement time; page unavailable) report that increasing cursor speed made movements fast but inaccurate, and the Introduction identifies signal-independent decoding noise as the root cause of the movement-time properties observed. This supports a speed–accuracy constraint in the intracortical cursor systems studied, but not the broader claim that decoding accuracy is the primary speed limit for point-and-click BCIs generally. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“EEG spellers based on oddball potentials or motor imagery typically achieve 1–5 characters per minute28–32.”
§ High-performance brain-to-text communication via handwriting > Discussion
What the sources say
- Could not verifycites [28] Vansteensel, M. J. et al. Fully implanted brain–computer interface in a locked-in patient with ALS. N. Engl. J. Med. 375, 2060–2066 (2016). sourceExperimental — accuracy not yet measured
The Results section, “Communication and Independent Home Use” (page unavailable), reports spelling times for one patient using a fully implanted interface. Our copy does not give a typical rate for EEG spellers based on oddball potentials or motor imagery; searches for those methods and their characters-per-minute rates found no such passage. (LLM, conf=0.76)
- Partly supportedcites [29] Nijboer, F. et al. A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clin. Neurophysiol. 119, 1909–1916 (2008). sourceExperimental — accuracy not yet measured
The Results report 1.2 and 2.1 selections per minute for the study’s P300 speller, which uses an oddball paradigm (Introduction). This supports the stated range for this speller in people with ALS, but the paper does not establish that EEG spellers generally achieve that rate or provide a rate for motor-imagery spellers. Page numbers were not available in our copy. (LLM, conf=0.86)
- Partly supportedcites [30] Townsend, G. et al. A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin. Neurophysiol. 121, 1109–1120 (2010). sourceExperimental — accuracy not yet measured
The Discussion (§4.2; page unavailable) reports 4.10 selections/min for a previously studied P300 speller and 4.36 selections/min for this paper’s checkerboard P300 paradigm, both within the claimed range. It does not establish that 1–5 characters/min is typical across EEG spellers or report a rate for motor-imagery spellers. (LLM, conf=0.84)
- Partly supportedcites [31] McCane, L. M. et al. P300-based brain–computer interface (BCI) event-related potentials (ERPs): people with amyotrophic lateral sclerosis (ALS) vs. age-matched controls. Clin. Neurophysiol. 126, 2124–2131 (2015). sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) identifies the P300 speller as an oddball-response system that provides slow communication. The Methods describe a characters-per-minute measure, and the Results report communication rates for this study, but the passages do not state that EEG spellers typically achieve 1–5 characters per minute or address motor-imagery spellers. (LLM, conf=0.77)
- Partly supportedcites [32] Wolpaw, J. R. et al. Independent home use of a brain–computer interface by people with amyotrophic lateral sclerosis. Neurology 91, e258–e267 (2018). sourceExperimental — accuracy not yet measured
The Results report an average of 2.9 (±1.3 SD) selections per minute for BCI applications used by people with ALS, within the claimed numerical range. The paper does not establish a typical rate of 1–5 characters per minute across EEG spellers based on oddball potentials or motor imagery; selections are also not necessarily characters. (LLM, conf=0.86)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For intracortical BCIs, the highest performing method has been point-and-click typing with a 2D computer cursor, peaking at 40 characters per minute 7”
§ High-performance brain-to-text communication via handwriting > Temporal variety improves decoding
What the sources say
- Partly supportedcites [7] Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6, e18554 (2017). sourceExperimental — accuracy not yet measured
The Introduction describes two-dimensional cursor control with click selection, and the Results say this study outperformed previous BCIs tested with people with motor impairment. The Results report T5’s average typing rate as 39.2 correct characters per minute, approximately 40, but do not identify 40 as a peak or an upper limit. (LLM, conf=0.85)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The prior state-of-the-art for free typing in intracortical BCIs was 24.4 correct characters per minute 7.”
§ High-performance brain-to-text communication via handwriting > Decoding handwritten sentences
What the sources say
- Supportedcites [7] Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6, e18554 (2017). source
The Results report T6’s mean free typing rate as 24.4 correct characters per minute and describe those sessions as achieving, to the authors’ knowledge, the highest typing rate to date for a person with a physical disability using a BCI. This supports the citing paper’s description of that rate as the prior benchmark for free typing. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“We used a time-alignment technique to remove temporal variability11, revealing remarkably consistent underlying patterns of neural activity that are unique to each character (Fig. 1c).”
§ High-performance brain-to-text communication via handwriting > Neural representation of handwriting
What the sources say
- Supportedcites [11] Williams, A. H. et al. Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping. Neuron 105, 246–259 (2020). source
The citation is attached to the time-alignment technique, not the citing authors’ character-specific finding. The source’s Results section (“Time Warping Framework”) describes trial-by-trial time warping to correct variability in the timing of neural activity. (LLM, conf=0.91)
boosteddirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Current microelectrode array technology has been shown to retain functionality for 1000+ days post implant37,38 (including here; see Extended Data Fig. 7)”
§ High-performance brain-to-text communication via handwriting > Discussion
What the sources say
- Supportedcites [37] Simeral, J. D., Kim, S.-P., Black, M. J., Donoghue, J. P. & Hochberg, L. R. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J. Neural Eng. 8, 025027 (2011). source
The Methods section (2.2) places the study sessions on implant days 999–1003. The Discussion (4.1) reports successful point-and-click control on day 1003, supporting retained array functionality beyond 1000 days. The parenthetical “including here” refers to the citing authors’ own study, not a finding attributed to this source. (LLM, conf=0.94)
- Not checked — no full text of the sourcecites [38] Bullard, A. J., Hutchison, B. C., Lee, J., Chestek, C. A. & Patil, P. G. Estimating risk for future intracranial, fully implanted, modular neuroprosthetic systems: a systematic review of hardware complications in clinical deep brain stimulation and experimental human intracortical arrays. Neuromodulation 23, 411–426 (2020). source
boostedSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“and has enabled the highest BCI performance to date compared to other recording technologies (EEG, ECoG) for restoring communication7, arm control2,5, and general-purpose computer use39.”
§ High-performance brain-to-text communication via handwriting > Discussion
What the sources say
- Supportedcites [7] Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6, e18554 (2017). source
Citation 7 is attached to restoring communication, not to arm control or general-purpose computer use, which have separate citations. In the Results (Table 1; page unavailable), the paper compares its intracortical communication BCI with prior systems, including EEG systems, and states that it outperforms all previous BCIs tested with people with motor impairment. (LLM, conf=0.90)
- Supportedcites [2] Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013). source
This citation is attached to arm control, not to communication or general-purpose computer use. The Methods (Array implantation; page unavailable) describe intracortical microelectrode arrays, and the Introduction (page unavailable) contrasts this study’s robust seven-dimensional prosthetic-arm control with earlier human studies limited to three-dimensional translation and discrete grasp. That supports the citing sentence’s arm-control comparison. (LLM, conf=0.86)
- Partly supportedcites [5] Ajiboye, A. B. et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. Lancet 389, 1821–1830 (2017). sourceExperimental — accuracy not yet measured
The Methods describe implanted microelectrode arrays controlling reaching and grasping, and the Discussion says intracortical BCIs offer greater functional performance than non-invasive systems, specifically contrasting robotic-arm control with EEG. The paper does not establish a performance comparison with ECoG. Communication and general-purpose computer use have separate citations in the citing sentence and are not judged against this source. (LLM, conf=0.84)
- Partly supportedcites [39] Nuyujukian, P. et al. Cortical control of a tablet computer by people with paralysis. PLoS One 13, e0204566 (2018). sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says prior EEG and ECoG work had not shown general control of a computing device such as a commercial tablet. The Abstract and Conclusion (pages unavailable) report high-performance control of an unmodified general-purpose tablet using implanted arrays. The paper does not establish that this achieved the highest BCI performance relative to EEG or ECoG; the separate communication and arm-control claims carry other citations in the citing sentence. (LLM, conf=0.84)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that when only 2–7 days passed between sessions, performance was reasonable with no decoder retraining (11.1% raw error rate, 1.5% with a language model)”
§ High-performance brain-to-text communication via handwriting > Daily decoder retraining
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Importantly, typing rates were high, plateauing at 90 characters per minute with a 5.4% character error rate (Fig. 2c, average of red circles).”
§ High-performance brain-to-text communication via handwriting > Decoding handwritten sentences
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 20
- Probably not a substantial claim. 8
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“which is comparable to state-of-the-art speech recognition systems (e.g. word error rates of 4–5% 14,15), putting it well within the range of usability.”
§ High-performance brain-to-text communication via handwriting > Decoding handwritten sentences
What the sources say
- Disputedcites [14] Xiong, W. et al. The Microsoft 2017 Conversational Speech Recognition System. Preprint at https://arxiv.org/abs/1708.06073 (2017). sourceExperimental — accuracy not yet measured
The Conclusions (p. 4) identify 6.2% as the state-of-the-art word error rate on the Switchboard test data, not 4–5%. The paper does not assess whether an error rate is usable; the citing sentence presents usability as its own inference. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
- Not checked — no full text of the sourcecites [15] He, Y. et al. Streaming end-to-end speech recognition for mobile devices. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing 6381–6385 (2019). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Disputed
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Prior BCI studies have shown that the motor intention for gross motor skills, such as reaching, grasping or moving a computer cursor, remains neurally encoded in motor cortex after paralysis1–7.”
§ High-performance brain-to-text communication via handwriting > Neural representation of handwriting
What the sources say
- Supportedcites [1] Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012). source
The Abstract reports that people with longstanding tetraplegia previously used neural signals to move and click a computer cursor. It also reports that two participants used motor-cortex activity generated by intended arm and hand movements to reach and grasp with a robotic arm, supporting the claim that these intentions remain neurally encoded after paralysis. (LLM, conf=0.91)
- Supportedcites [2] Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013). source
The Methods describe intracortical electrodes implanted in the motor cortex of a person with tetraplegia, and the Introduction reports neural control of reaching and grasping. The Discussion also reports earlier human intracortical studies controlling computer cursors. The copy provides section labels but no page numbers. (LLM, conf=0.91)
- Partly supportedcites [3] Aflalo, T. et al. Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348, 906–910 (2015). sourceExperimental — accuracy not yet measured
The passage comparing this study with previous human M1 recordings (indexed section labeled Abstract; page unavailable) reports that those studies decoded movement trajectories, supporting the motor-cortex portion in part. This paper’s own recordings of imagined reaching, grasping, and cursor control were from the posterior parietal cortex of a tetraplegic participant, not motor cortex. It does not establish that the earlier motor-cortex studies demonstrated all three movements named in the claim. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites [4] Bouton, C. E. et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 533, 247–250 (2016). source
- Supportedcites [5] Ajiboye, A. B. et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. Lancet 389, 1821–1830 (2017). source
The Introduction reports that people with paralysis had used intracortical BCIs to control cursors and robotic reaching and grasping. The Methods locate the arrays in motor cortex, and the Results report neural activity strongly related to intended movements in the participant with tetraplegia. Together, these passages support the claim that movement intentions remain neurally encoded after paralysis. (LLM, conf=0.90)
- Supportedcites [6] Jarosiewicz, B. et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain–computer interface. Sci. Transl. Med. 7, 313ra179 (2015). source
The Introduction reports that people with tetraplegia have controlled cursors and robotic arms through intracortical BCIs by imagining arm movement. The Results locate the recorded neural signals in the hand/arm area of motor cortex, and the References identify an earlier study of neurally controlled reaching and grasping by people with tetraplegia. No page numbers are available in our copy. (LLM, conf=0.88)
- Partly supportedcites [7] Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6, e18554 (2017). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) reports that people with no limb movement controlled a cursor using an intracortical BCI, and that monkeys given a paralytic agent retained grasp-related cortical activity. Our copy does not establish the claimed post-paralysis encoding of intended reaching; its mention of reaching is in a reference title about primates. (LLM, conf=0.82)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“as might be expected from prior work showing short-term stability of neural recordings19–21.”
§ High-performance brain-to-text communication via handwriting > Daily decoder retraining
What the sources say
- Supportedcites [19] Dickey, A. S., Suminski, A., Amit, Y. & Hatsopoulos, N. G. Single-unit stability using chronically implanted multielectrode arrays. J. Neurophysiol. 102, 1331–1339 (2009). source
The Abstract reports that 57% of the original units remained stable through 7 days, and the Discussion concludes that a subset of neurons remained stable for days to weeks. This supports the cited point about short-term stability of neural recordings; the finding concerns a subset of recorded units, not every unit. Page numbers were not provided in the supplied passages. (LLM, conf=0.93)
- Supportedcites [20] Eleryan, A. et al. Tracking single units in chronic, large scale, neural recordings for brain machine interface applications. Front. Neuroeng. 7, 23 (2014). source
The Discussion reports stable single units across recording days and describes this paper’s method for tracking them using 15-minute recordings. That supports the broad claim of short-term stability, though the paper also notes that neural signals can fluctuate and not every unit remains stable. No page number is available in our copy. (LLM, conf=0.84)
- Supportedcites [21] Downey, J. E., Schwed, N., Chase, S. M., Schwartz, A. B. & Collinger, J. L. Intracortical recording stability in human brain–computer interface users. J. Neural Eng. 15, 046016 (2018). source
The Results section, “Most units are stable for hours,” reports that 72%–80% of recorded units remained stable across recordings one hour apart in both subjects. This supports the cited claim of short-term stability without implying that every recording was stable. (LLM, conf=0.94)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“More generally, using the principle of maximizing the nearest neighbor distance between movements, it should be possible to optimize a set of movements for ease of classification26.”
§ High-performance brain-to-text communication via handwriting > Temporal variety improves decoding
What the sources say
- Supportedcites [26] Cunningham, J. P., Yu, B. M., Gilja, V., Ryu, S. I. & Shenoy, K. V. Toward optimal target placement for neural prosthetic devices. J. Neurophysiol. 100, 3445–3457 (2008). source
The Methods section states that the least distinguishable pair of reach targets has the smallest KL divergence and that maximizing this minimum divergence reduces decoding error. This supports optimizing a set of movements for easier classification; the relevant distance is between their neural-response distributions, not necessarily their physical positions. (LLM, conf=0.88)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Variability in performance across participants is also a potential concern (in a prior study, T5 achieved the highest performance of 3 participants7).”
§ High-performance brain-to-text communication via handwriting > Discussion
What the sources say
- Supportedcites [7] Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6, e18554 (2017). source
The Results report performance for all three participants: T5 had the highest average grid-task bitrate (3.7 bps, versus 2.2 for T6 and 1.4 for T7). The Results also show T5 had the highest reported average typing rate, while the Discussion explicitly notes variation in communication rates across participants. (LLM, conf=0.94)
hedgedn = 3- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Recently, speech-decoding BCIs have shown exciting promise for restoring rapid communication (e.g. 34,16,17), but their accuracies and vocabulary sizes require further improvement to support general-purpose use.”
§ High-performance brain-to-text communication via handwriting > Discussion
What the sources say
- Supportedcites [34] Mugler, E. M. et al. Direct classification of all American English phonemes using signals from functional speech motor cortex. J. Neural Eng. 11, 035015 (2014). source
The Abstract reports word identification equivalent to as much as 33.6 words per minute, supporting the promise of speech-based BCI communication. The Discussion says word identification was only 18.8% and was not sufficient for communication, while describing broader word decoding as a future step. Together, these support the claim that accuracy and vocabulary coverage need improvement for general-purpose use. (LLM, conf=0.91)
- Supportedcites [16] Anumanchipalli, G. K., Chartier, J. & Chang, E. F. Speech synthesis from neural decoding of spoken sentences. Nature 568, 493–498 (2019). source
The Abstract and Discussion describe neural speech synthesis as a promising route to communication at a natural speaking rate. Synthesis performance reports testing with only 25- or 50-word choice pools and median word error rates of 31% and 53%, respectively. Those limits support the claim that accuracy and vocabulary would need improvement for general-purpose use. (LLM, conf=0.85)
- Supportedcites [17] Makin, J. G., Moses, D. A. & Chang, E. F. Machine translation of cortical activity to text with an encoder-decoder framework. Nat. Neurosci. 23, 575–582 (2020). source
The Abstract reports decoding at natural-speech rates with word error rates as low as 3%. The Discussion qualifies that result: testing was limited to 30–50 sentences and a 250-word vocabulary, and it remains unknown how much data would be needed to extend the approach to general English. Page numbers were not provided in our copy. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“EEG spellers that use visually evoked potentials have achieved speeds of 60 characters per minute 18, but have important usability limitations, as they tie up the eyes, are not typically self-paced, and require panels of flashing lights on a screen.”
§ High-performance brain-to-text communication via handwriting > Discussion
What the sources say
- Supportedcites [18] Chen, X. et al. High-speed spelling with a noninvasive brain–computer interface. Proc. Natl Acad. Sci. USA 112, E6058–E6067 (2015). source
The citation is attached to the 60-characters-per-minute figure. The Results report that this EEG-based SSVEP speller achieved that rate; the subsequent usability characterization is not the part directly attributed to this source. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Intracortical BCIs based on 2D cursor movements give the user more freedom to look around and set their own pace of communication, but have yet to exceed 40 correct characters per minute in people7.”
§ High-performance brain-to-text communication via handwriting > Discussion
What the sources say
- Partly supportedcites [7] Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6, e18554 (2017). sourceExperimental — accuracy not yet measured
The Introduction describes continuous two-dimensional cursor control, and the Discussion describes the point-and-click interface as flexible. The Results report average copy-typing rates up to 39.2 correct characters per minute, but do not establish that no individual typing rate exceeded 40. The cited passages also do not establish the specific claim that users can look around while communicating. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The neural activity appeared to be strong and repeatable, although the timing of its peaks and valleys varied across trials, potentially due to fluctuations in writing speed.”
§ High-performance brain-to-text communication via handwriting > Neural representation of handwriting
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We assessed the stability of the neural patterns associated with each character and found high short-term stability (mean correlation = 0.85 when 7 days apart or less), and neural changes that seemed to accumulate at a steady and predictable rate (Extended Data Fig. 4).”
§ High-performance brain-to-text communication via handwriting > Daily decoder retraining
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accuracy was extremely high in this regime (0.17% character error rate), indicating a high potential ceiling of performance, although this decoder would not be able to provide letter-by-letter feedback to the user.”
§ High-performance brain-to-text communication via handwriting > Decoding handwritten sentences
hedgedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Readily recognizable letter shapes confirm that pen tip velocity is robustly encoded.”
§ High-performance brain-to-text communication via handwriting > Neural representation of handwriting
boostedcorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that spatial dimensionality was only modestly larger for characters (1.24 times larger; 95% CI = [1.19, 1.30]), but that the temporal dimensionality was much greater (2.65 times larger; 95% CI = [2.58, 2.72])”
§ High-performance brain-to-text communication via handwriting > Temporal variety improves decoding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The character error rate fell to 0.89% and the word error rate fell to 3.4% averaged across all days”
§ High-performance brain-to-text communication via handwriting > Decoding handwritten sentences
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The t-SNE visualization revealed tight clusters of neural activity for each character and a predominantly motoric encoding where characters that are written similarly are closer together.”
§ High-performance brain-to-text communication via handwriting > Neural representation of handwriting
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“T5 has a high-level spinal cord injury (C4 AIS C) and was paralyzed from the neck down; his hand movements were entirely non-functional and limited to twitching and micromotion.”
§ High-performance brain-to-text communication via handwriting > Neural representation of handwriting
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Simply by allowing the trajectories to change in time (Fig. 4g), the nearest neighbor distance between the neural trajectories can be increased, resulting in an increase in classification accuracy when noise levels are large enough (Fig. 4h).”
§ High-performance brain-to-text communication via handwriting > Temporal variety improves decoding
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results confirm that high performance can also be achieved when the user writes self-generated sentences as opposed to copying on-screen prompts (73.8 characters per minute with an 8.54% character error rate in real-time, 2.25% with a language model).”
§ High-performance brain-to-text communication via handwriting > Decoding handwritten sentences
boostedSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that 10 sentences (8.7 minutes) were enough to achieve a raw error rate of 8.5% (1.7% with a language model), although 30 sentences were needed to match the raw online error rate of 5.9%.”
§ High-performance brain-to-text communication via handwriting > Daily decoder retraining
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We think this is one, but not necessarily the only, important factor that enabled a handwriting BCI to go faster than continuous-motion point-and-click BCIs.”
§ High-performance brain-to-text communication via handwriting > Temporal variety improves decoding
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Retraining helps account for changes in neural recordings that accrue over time, which might be caused by neural plasticity or electrode array micromotion.”
§ High-performance brain-to-text communication via handwriting > Daily decoder retraining
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Other discrete (classification-based) BCIs have also typically used directional movements with little temporal variety, which may have limited their accuracy and/or the size of the movement set24,25.”
§ High-performance brain-to-text communication via handwriting > Temporal variety improves decoding
What the sources say
- Not checked — no full text of the sourcecites [24] Musallam, S., Corneil, B. D., Greger, B., Scherberger, H. & Andersen, R. A. Cognitive control signals for neural prosthetics. Science 305, 258–262 (2004). source
- Not checked — no full text of the sourcecites [25] Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A. & Shenoy, K. V. A high-performance brain–computer interface. Nature 442, 195–198 (2006). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The demonstrated real-time system is general (the user can express any sentence), easy to use (entirely self-paced and the eyes are free to move), and accurate enough to be useful in the real-world (94.1% raw accuracy, and >99% accuracy offline with a large-vocabulary language model).”
§ High-performance brain-to-text communication via handwriting > Discussion
boostedSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These methods could be applied more generally to any sequential behavior that cannot be observed directly (e.g., decoding speech from someone who can no longer speak).”
§ High-performance brain-to-text communication via handwriting > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“More broadly, intracortical microelectrode array technology is still maturing, and requires further demonstrations of longevity, safety, and efficacy before widespread clinical adoption35,36.”
§ High-performance brain-to-text communication via handwriting > Discussion
What the sources say
- Not checked — no full text of the sourcecites [35] Nurmikko, A. Challenges for large-scale cortical interfaces. Neuron 108, 259–269 (2020). source
- Not checked — no full text of the sourcecites [36] Vázquez-Guardado, A., Yang, Y., Bandodkar, A. J. & Rogers, J. A. Recent advances in neurotechnologies with broad potential for neuroscience research. Nat. Neurosci. 23, 1522–1536 (2020). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, it is important to recognize that the current system is a proof-of-concept that a high-performance handwriting BCI is possible (in a single participant); it is not yet a complete, clinically viable system.”
§ High-performance brain-to-text communication via handwriting > Discussion
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
It was not yet known whether the neural representation for a rapid, highly dexterous skill such as handwriting remains intact after paralysis.
“However, it is still unknown whether the neural representation for a rapid and highly-dexterous motor skill, such as handwriting, also remains intact.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“which is 3.4 times as fast as the previous record8”
From A high-performance speech neuroprosthesis (2023) · cites this paper as [8]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“During 14 days of data collection shortly after device implantation, we achieved high-performance text decoding, exceeding communication speeds of previous BCIs by a factor of 4 or more1,3,9”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [9]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A second approach, specific to text decoding, involves leveraging large language models to automatically recalibrate MEA-based text neuroprostheses.”
From The speech neuroprosthesis (2024) · cites this paper as [9]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A similar tradeoff can be facilitated by having a spelling framework available on the neuroprosthesis, either using attempted speech120 or handwriting9, which would allow users to spell out arbitrary sentences with high accuracy (albeit with substantially lower speeds).”
From The speech neuroprosthesis (2024) · cites this paper as [9]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“This decoding rate exceeds our participant’s typical communication rate using her assistive device (14.2 WPM; Supplementary Note 2) and is closer to naturalistic speaking rates than has been previously reported with communication neuroprostheses1,3,7–9.”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [9]
The cited source says what the claim attributes to it.
In the citing paper
“In this approach, MEA activity is decoded into subword units and converted into sentences using language models; this language model output is used as a ‘pseudo-label’ to update the RNN decoding model9,16,105.”
From The speech neuroprosthesis (2024) · cites this paper as [9]
The cited source says what the claim attributes to it.
In the citing paper
“MEA-based decoders, including text decoders9,15, have traditionally required day-to-day recalibration owing to signal instabilities and drift, requiring additional dedicated time from the user.”
From The speech neuroprosthesis (2024) · cites this paper as [9]
The cited source says what the claim attributes to it.
In the citing paper
“and several studies indicate that UEAs retain their functionality in humans for more than 1000 days (29, 31).”
From Visual percepts evoked with an intracortical 96-channel microelectrode array inserted in human occipital cortex (2021) · cites this paper as [29]
The cited source says what the claim attributes to it.
In the citing paper
“Although the duration of this study was limited to 6 months, UEAs have been successfully implanted in human subjects in numerous studies (25-29) and these devices have continued to work for many years.”
From Visual percepts evoked with an intracortical 96-channel microelectrode array inserted in human occipital cortex (2021) · cites this paper as [29]
The cited source says what the claim attributes to it.
In the citing paper
“Recently, BCIs based on hand movement activity have enabled typing speeds of between eight and 18 words per minute in people with paralysis8,29.”
From A high-performance speech neuroprosthesis (2023) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“Neurotechnologies can decode and project very specific and often rudimentary forms of thought, such as imagined handwriting, 40 typing, 27 and other kinds of intended and directed movements. 28”
From AI and Neurotechnology (2023) · cites this paper as [40]
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 3×
“We used the gradient descent method “Adam” (Kingma and Ba, 2017) (beta1 = 0.”
Method(2008) · Journal of Machine Learning Research · Cited by 33,051
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · cited 1×
“We used a two layer, gated recurrent unit RNN (Cho et al., 2014) to convert T5’s neural activity into a time series of character probabilities (see Extended Data Fig.”
MethodHinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · cited 2×
“We used the “Adam” gradient descent method (Abadi et al., 2016; Kingma and Ba, 2017).”
MethodSpeech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · cited 1×
“Our methods are similar to neural network methods used in automatic speech recognition (Hinton et al., 2012; Graves et al., 2013; Zeyer et al., 2017; Xiong et al., 2017), but with some key changes made to achieve high performance on neural activity in a highly data-limited regime (1-10 hours of data, as opposed to 1,0…”
Method“Librispeech: an ASR corpus based on public domain audio books,”
Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072 · cited 1×
“, 2017), but with some key changes made to achieve high performance on neural activity in a highly data-limited regime (1-10 hours of data, as opposed to 1,000+ hours (Cieri et al., 2004; Panayotov et al., 2015; He et al., 2019)).”
BackgroundGraves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370
Kaldi Speech Recognition Toolkit
Povey, D. (2024) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.57702/jb3fvbn9 · Cited by 5,270
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
Self-citation
Cited by
Sorted by citations
Items 301–400 of 967
Neuromorphic computing facilitates deep brain-machine fusion for high-performance neuroprosthesis
Qi, Y., Chen, J., & Wang, Y. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1153985 · Cited by 14
“Traditionally, neuronal spike trains are transformed into continuous values in temporal bins to be fed into decoders (Hochberg et al., 2012; Willett et al., 2021), where the precise timing and spike order between neurons are inevitably lost.”
BackgroundDecision Aids in the ICU: a scoping review
Lei, Y., Zhou, Q., & Tao, Y. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-075239 · Cited by 11
Fully implantable wireless brain-computer interface for humans: Advancing toward the future
Zhang, Z., & Dai, J. (2024) · The Innovation · doi:10.1016/j.xinn.2024.100595 · Cited by 11
Mokienko, O. A. (2024) · Современные технологии в медицине · doi:10.17691/stm2024.16.1.08 · Cited by 12
Li, M., Li, N., Gao, X., Ma, R., Dong, J., Chen, X., & Cui, H. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3241629 · Cited by 11
“With recent advancements in brain science and computer technology, the field of BCI has also been developing rapidly [5], [6].”
Sawyer, A., Cooke, L., Ramsey, N. F., & Putrino, D. F. (2023) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2023-020316 · Cited by 11
Chen, Y., Wang, F., Li, T., Zhao, L., Gong, A., Nan, W., Ding, P., & Fu, Y. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1391550 · Cited by 13
Yeom, H. G., Kim, J. S., & Chung, C. K. (2023) · Scientific Data · doi:10.1038/s41597-023-02454-y · Cited by 12
“Thus, multiple studies have investigated brain mechanisms 1,5–9 and developed the BCIs 4,10 .”
BackgroundRecommendations for promoting user agency in the design of speech neuroprostheses
Sankaran, N., Moses, D., Chiong, W., & Chang, E. F. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1298129 · Cited by 13
Zhu, J. L., Xu, G., Hu, Q., Wang, B., Zhou, T., & Qin, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3341121 · Cited by 10
Unveiling Recent Trends in Biomedical Artificial Intelligence Research: Analysis of Top-Cited Papers
Glicksberg, B. S., & Klang, E. (2024) · Applied Sciences · doi:10.3390/app14020785 · Cited by 12
MouseClicker: Exploring Tactile Feedback and Physical Agency for People with Hand Motor Impairments
Taheri, A., Gomez-Monroy, C. G., Borja, V., & Sra, M. (2024) · ACM Transactions on Accessible Computing · doi:10.1145/3648685 · Cited by 10
“BCIs have been used for brain-to-text communication [Willett et al. 2021 ] and hands-free wheelchair control [Singla et al. 2014 ] have enabled interaction with computers and movement without assistance, empowering individuals with paralysis or motor impairments.”
MethodEnd-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging
Ajioka, T., Nakai, N., Yamashita, O., & Takumi, T. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011074 · Cited by 10
“Recently, attempted speech and handwriting movements have been decoded on the temporal scales in real-time from the cortical activity obtained by microelectrode array and electrocorticography (ECoG) from human patients [5,29,30].”
BackgroundLiu, T., Chua, Y., Ning, Y., Liu, P., Zhang, Y., Li, T., Wan, Z., Wan, Z., Chen, W., & Zhang, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106745 · Cited by 10
Mohan, V., Tay, W. P., & Basu, A. (2025) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/adad10 · Cited by 13
“Recent works using iBMI have enabled brain to handwritten text [7], brain-based speech synthesis [8], and therapies for epilepsy via deep brain stimulation and mental disorders [9].”
BackgroundLong-term independent use of an intracortical brain–computer interface for speech and cursor control
Card, N. S., Singer-Clark, T., Peracha, H., Iacobacci, C., Hou, X., Wairagkar, M., Fogg, Z., Offenberg, E. C., Hochberg, L. R., Stavisky, S. D., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04414-6 · Cited by 24
“Intracortical BCIs, which record neural signals at the resolution of action potentials, have enabled the highest-performance decoding to date of cursor 9 , handwriting 6 , typing 13 and speech 5,12 .”
Self-citationClassification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
Kerchove, A. V. D., Libert, A., Wittevrongel, B., & Hulle, M. M. V. (2022) · Applied Sciences · doi:10.3390/app12062918 · Cited by 9
“Although invasive BCIs yield the highest information transfer rate [10], non-invasive BCIs are preferable for short-term use since they are not susceptible to the risks that come with surgery.”
Minimally Invasive Local-Skull Electrophysiological Modification With Piezoelectric Drill
Sun, Y., Shen, A., Sun, J., Du, C., Chen, X., Wang, Y., Pei, W., & Gao, X. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3192543 · Cited by 10
Design of virtual BCI channels based on informer
Sun, H., Li, C., & Zhang, H. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1150316 · Cited by 11
“Willett et al. (2021) achieved previously science-fictional technologies, such as writing with thoughts.”
BackgroundImagined, Intended, and Spoken Speech Envelope Synthesis from Neuromagnetic Signals
Dash, D., Ferrari, P., Berstis, K., & Wang, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87802-3_13 · Cited by 9
Lech, M., Czyżewski, A., & Kucewicz, M. T. (2021) · Sensors · doi:10.3390/s21227605 · Cited by 11
Yang, C., Zhang, Y., Chang, Z., Li, Z., Gao, H., Luo, Y., Xu, K., Pan, G., & Zhao, B. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2022.3232783 · Cited by 12
“for neurological diseases, such as communication disability [1], sports dysfunction [2], [3], epilepsy, etc.”
BackgroundRizzoglio, F., Altan, E., Ma, X., Bodkin, K. L., Dekleva, B. M., Solla, S. A., Kennedy, A., & Miller, L. E. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad038e · Cited by 12
“…studies, deep learning-based decoders could be used for this purpose, as they have been shown to be more robust to variability in recording conditions and have higher decoding accuracy compared to linear alternatives (Glaser et al 2020, Willett et al 2021, 2023, Willsey et al 2022, Ye et al 2023).”
BackgroundBalancing Memorization and Generalization in RNNs for High Performance Brain-Machine InterfacesPreprint
Costello, J. T., Temmar, H., Cubillos, L. H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.28.542435 · Cited by 10
“Adding a small amount of noise to the neural data during training (as suggested in [28]) improved average trial times by 6 % .”
BackgroundHahn, N. V., Stein, E. A., Donoghue, J. P., Simeral, J. D., Hochberg, L. R., & Willett, F. R. (2025) · medRxiv · doi:10.1101/2025.07.02.25330310 · Cited by 15
“…robot control (Hochberg et al. 2012; Collinger et al. 2013; Benabid et al. 2019), functional electrical stimulation (Ajiboye et al. 2017), typing (Willett et al. 2021; Vansteensel et al. 2016, 2024; Shah et al. 2024; Jude et al. 2025), and speech (Moses et al. 2021; Willett et al. 2023; Metzger…”
Self-citationWilson, G. H., Stein, E. A., Kamdar, F. B., Avansino, D. T., Pun, T. K., Gross, R., Hosman, T., Singer-Clark, T., Kapitonava, A., Hochberg, L. R., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01536-z · Cited by 30
Self-citationLou, Z., Wang, R., Belmekki, B. E. Y., & Alouini, M. (2026) · npj Biomedical Innovations. · doi:10.1038/s44385-025-00061-7 · Cited by 17
A Moral Imperative to Advance Brain-Computer Interfaces for Children With Neurological Disability
Kirton, A. (2023) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2023.1744 · Cited by 9
Low-invasive neural recording in mouse models with diabetes via an ultrasmall needle-electrode
Sanda, R., Yamashita, K., Sawahata, H., Sakamoto, K., Yamagiwa, S., Yokoyama, S., Numano, R., Koida, K., & Kawano, T. (2023) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2023.115605 · Cited by 8
Li, C., Wang, H., Liu, Y., Zhu, X., & Song, L. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105524 · Cited by 9
Song, S. S., Druschel, L. N., Conard, J. H., Wang, J., Kasthuri, N. M., Chan, E. R., & Capadona, J. R. (2024) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2024.03.004 · Cited by 9
“For example, the recorded neural activity can restore lost function in paralyzed and injured individuals (Fatima et al., 2020; Irwin, 2017; Pandarinath, 2017; Paulk, 2022; Rapeaux and Constandinou, 2021; Simeral, 2011; Willett, 2021).”
An optimized EEGNet decoder for decoding motor image of four class fingers flexion
Rao, Y., Zhang, L., Jing, R., Huo, J., Yan, K., He, J., Hou, X., Mu, J., Geng, W., Cui, H., et al. (2024) · Brain Research · doi:10.1016/j.brainres.2024.149085 · Cited by 10
Challenges and future directions of SUDEP models
Gu, J., Shao, W., Liu, L., Wang, Y., Yang, Y., Zhang, Z., Wu, Y., Xu, Q. Y., Gu, L., Zhang, Y., et al. (2024) · Lab Animal · doi:10.1038/s41684-024-01426-y · Cited by 10
Technical survey of end-to-end signal processing in BCIs using invasive MEAs
Erbslöh, A., Buron, L., Ur-Rehman, Z., Musall, S., Hrycak, C., Löhler, P., Klaes, C., Seidl, K., & Schiele, G. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad8031 · Cited by 11
“Here, brain-computer interfaces (BCIs) in the motor cortex can be used to record neural population activity and directly decode intended speech or handwriting patterns [138].”
Handwritten character classification from EEG through continuous kinematic decoding
Crell, M. R., & Müller-Putz, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109132 · Cited by 12
Peripheral neural interfaces for reading high-frequency brain signals
Ibáñez, J., Zicher, B., Burdet, E., Baker, S. N., Mehring, C., & Farina, D. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01445-1 · Cited by 15
“However, there is growing evidence of increasingly safe approaches, such as intracranial recordings used in various medical devices, which have led to meaningful advancements in fields such as speech prostheses and other types of brain–machine interface for patients with severe paralysis 3,4 .”
Brain–computer interfaces patient preferences: a systematic review
Brannigan, J., Liyanage, K., Horsfall, H. L., Bashford, L., Muirhead, W. R., & Fry, A. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad94a6 · Cited by 13
“Whilst this approach resulted in a median word error rate of 25% and was limited to a 1024-word vocabulary, other approaches have generated language outputs at 90 characters per minute with no vocabulary limitations and 99% accuracy [36].”
Closed-loop modulation of remote hippocampal representations with neurofeedback
Coulter, M. E., Gillespie, A. K., Chu, J. P., Denovellis, E. L., Nguyen, T. T. K., Liu, D. F., Wadhwani, K., Sharma, B., Wang, K. X.-L., Deng, X., et al. (2025) · Neuron · doi:10.1016/j.neuron.2024.12.023 · Cited by 14
“Lead contact: Loren Frank, loren.frank@ucsf.edu Materials Availability: Not applicable. information required to reanalyze the data reported in this paper is available from the lead contact upon request.”
Xu, Q., Wang, L., Xi, Y., Ruan, T., Cao, J., Xu, M., Zheng, K., Du, Z., Wei, N., Wang, X., et al. (2025) · Small · doi:10.1002/smll.202407950 · Cited by 10
Han, K., Joung, J. F., Han, M., Sung, W., & Kang, Y.-N. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020143 · Cited by 9
Gadamer in a Wired Brain: Philosophical Hermeneutics and Neuralink
Lindia, M. S. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00522-6 · Cited by 8
“Current BCI researchers have developed methods for translating mental visualizations of handwriting into digital text (Willett et al., 2021), translating cortical activity of imagined words into acoustic speech (Anumanchipalli et al., 2019), translating imagined words into text (Makin et al.,…”
BackgroundMethodOn The Verge of the Hybrid Mind
Soekadar, S. R., Chandler, J. A., Ienca, M., et al. (2021) · Morals & Machines · doi:10.5771/2747-5174-2021-1-30 · Cited by 9
Spike prediction on primary motor cortex from medial prefrontal cortex during task learning
Wu, S., Qian, C., Shen, X., Zhang, X., Huang, Y., Chen, S., & Wang, Y. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac8180 · Cited by 10
“Researchers use microelectrode arrays to record neural spikes from motor-related brain regions and design decoders to interpret the signal to control external devices [3–5].”
Reengineering neurotechnology: placing patients first
Ploner, M., Buyx, A., Gempt, J., Gjorgjieva, J., Müller, R., Priller, J., Rückert, D., Wolfrum, B. L., & Jacob, S. N. (2023) · Nature Mental Health · doi:10.1038/s44220-022-00011-x · Cited by 10
Rubin, D. B., & Hochberg, L. R. (2023) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci57258.2023.10078547 · Cited by 9
“By training a recurrent neural network to accurately discriminate the cortical activity associated with the attempted writing of each character of the English alphabet, the BrainGate system was used to decode sentences written at 90 correct characters per minute, more than double the rate achieved through pointand-cli…”
MethodSelf-citationA brain-computer typing interface using finger movements
Shah, N. P., Willsey, M. S., Hahn, N. V., Kamdar, F. B., Avansino, D. T., Hochberg, L. R., Shenoy, K. V., & Henderson, J. M. (2023) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner52421.2023.10123912 · Cited by 10
“Second, a novel nonlinear decoding architecture may lead to shorter target acquisition times [3], [8].”
BackgroundMethodSelf-citationMachine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review
Sen, O., Sheehan, A. M., Raman, P. R., Khara, K. S., Khalifa, A., & Chatterjee, B. (2023) · Sensors · doi:10.3390/s23125575 · Cited by 11
“In [81], RNNs were used to convert the neural activity into probabilities that describe the likelihood of characters that will be written.”
InfluentialGroup‐level brain decoding with deep learning
Csáky, R., van Es, M. W. J., Jones, Ō. P., & Woolrich, M. W. (2023) · Human Brain Mapping · doi:10.1002/hbm.26500 · Cited by 9
“Such analyses can be useful for brain-computer interface (BCI) applications (Willett et al., 2021) or to gain neuroscientific insights (Cichy et al., 2016; Guggenmos et al., 2018; Kay et al., 2008).”
BackgroundIwama, S., & Ushiba, J. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3365197 · Cited by 9
“B RAIN-COMPUTER interfaces (BCIs) translate neural signals into action intention, opening an alternative pathway for users to express their will such as movement intention [1], [2], [3], [4], [5], [6], [7].”
BackgroundA Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding
Liu, X., Chen, S., Shen, X., Zhang, X., & Wang, Y. (2021) · Entropy · doi:10.3390/e23060743 · Cited by 11
“The recurrent neural network (RNN) model considers the possible internal dynamics and has been used to decode the letter shapes for classification [37].”
MethodFast construction of interpretable whole-brain decoders
Lee, S., Bradlow, E. T., & Kable, J. W. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100227 · Cited by 8
“In applied research, researchers have built brain-computer interfaces (BCIs) that read real-time neural activity to control robotic arms (Hochberg et al., 2012) or to type computer text (Willett et al., 2021) for patients who are paralyzed.”
BackgroundYi, W., Chen, J., Wang, D., Hu, X., Xu, M., Li, F., Wu, S., & Qian, J. (2025) · Scientific Data · doi:10.1038/s41597-025-05286-0 · Cited by 9
Wireless interfaces for brain neurotechnologies
Kim, H., & Ho, J. S. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0020 · Cited by 12
“Most brain-computer interfaces (BCIs) clinically tested in humans, however, have continued to rely on wired interfaces owing to limitations in the bandwidth and reliability of wireless communication [30,31,35].”
BackgroundSpatiotemporal patterns in FitzHugh–Nagumo network and its application in image encryption
Yao, Z., Sun, K., & Wang, H. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108204 · Cited by 9
Flexible kirigami microelectrode arrays for neuronal activity recordings in non-human primate brains
FANG, R., Tian, H., Du, Y., Zhao, Y., Yang, Y., Guan, S., Li, S., Liu, M., Xu, K., & Fang, Y. W. (2026) · Nature Electronics · doi:10.1038/s41928-025-01560-6 · Cited by 9
Preferential modulation of individual retinal ganglion cells by electrical stimulation
Yunzab, M., Soto-Breceda, A., Maturana, M. I., Kirkby, S., Slattery, M., Newgreen, A., Hamish Meffin, A., Kameneva, T., Burkitt, A. N., Ibbotson, M. R., et al. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac861f · Cited by 10
Hardware evaluation of spike detection algorithms towards wireless brain machine interfaces
Oprea, A., Zhang, Z., & Constandinou, T. G. (2022) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas54905.2022.9948632 · Cited by 9
Research on Perceptron Neural Network Based on Memristor
Wen, C., Zha, J., Xu, L., et al. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3319747 · Cited by 8
“Atthesametime,artificialneural networktechnologywillalsobeoneofthepotentialtechnolo-giesforhumanbeingstoestablishbraincomputerinteraction interface[4],[5].”
The rise of supercapacitor diodes: Current progresses and future challenges
Ma, H., Ma, L., Bi, H., & Lan, W. (2023) · Chinese Physics B · doi:10.1088/1674-1056/ad1171 · Cited by 9
Beta bursts question the ruling power for brain–computer interfaces
Papadopoulos, S., Szul, M. J., Congedo, M., Bonaiuto, J. J., & Mattout, J. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad19ea · Cited by 8
Adversarial Neural Network Training for Secure and Robust Brain-to-Brain Communication
Ahmadi, H., Kuhestani, A., & Mesin, L. (2024) · IEEE Access · doi:10.1109/access.2024.3376657 · Cited by 8
NeuroAiR: Deep Learning Framework for Airwriting Recognition From Scalp-Recorded Neural Signals
Tripathi, A., Gupta, A., Prathosh, A. P., Muthukrishnan, S. P., & Kumar, L. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3381720 · Cited by 8
“In addition, the decoding of ECoG signals during handwriting imagery has shown promising results in typing applications, achieving high performance in converting imagined handwriting into text [29].”
BackgroundMachine learning decoding of single neurons in the thalamus for speech brain-machine interfaces
Tankus, A., Rosenberg, N., Ben-Hamo, O., Stern, E., & Strauss, I. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad4179 · Cited by 9
“Few studies attempted to restore higher cognitive functions (see reviews [11, 12]), provide the user with spelling devices based on direct phoneme decoding [13] or imagined handwriting decoding [14], or classification of code-words that symbolize letters [15].”
BackgroundEthical and social aspects of neural prosthetics
Glannon, W. (2021) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ac23e6 · Cited by 10
“One study used an intracortical BCI to record brainwaves associated with the thought of writing and translate them into computer-generated letters (Willett et al 2021).”
Awasthi, P., Lin, T.-H., Bae, J., Miller, L. E., & Danziger, Z. C. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac97c3 · Cited by 7
Kafaee, M., Daviran, E., & Taqavi, M. (2022) · AI & Society · doi:10.1007/s00146-022-01573-1 · Cited by 9
Co-Adaptive Myoelectric Interface for Continuous Control*
Madduri, M. M., Yamagami, M., Millevolte, A. X. T., Li, S., Burckhardt, S. N., Burden, S. A., & Orsborn, A. L. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.01.109 · Cited by 8
“…interfaces to restore function (BCIs) (Carmena, 2013; Shanechi et al., 2017), human-machine interfaces (HMIs) (De Santis, 2021), rehabilitation (Reinkensmeyer et al., 2016; Li et al., 2016), neu-roprosthetics (Hochberg et al., 2012), and augmenting human capabilities (Willett et al., 2021).”
BackgroundSun, Y., Shen, A., Du, C., Sun, J., Chen, X., & Gao, X. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3311750 · Cited by 8
“recognition tasks concentrate on the parietal regions [35], [36].”
BackgroundNeural interface translates thoughts into type
Rajeswaran, P., & Orsborn, A. L. (2021) · Nature · doi:10.1038/d41586-021-00776-8 · Cited by 8
Robust Online Multiband Drift Estimation in Electrophysiology DataPreprint
Windolf, C., Paulk, A. C., Kfir, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.04.519043 · Cited by 8
An emerging view of neural geometry in motor cortex supports high-performance decodingPreprint
Perkins, S. M., Amematsro, E. A., Cunningham, J. P., Wang, Q., & Churchland, M. M. (2023) · eLife · doi:10.7554/elife.89421 · Cited by 12
Aligned and oblique dynamics in recurrent neural networksPreprint
Schuessler, F., Mastrogiuseppe, F., Ostojic, S., & Barak, O. (2024) · eLife · doi:10.7554/elife.93060 · Cited by 11
“Both input and output can be decoded from population activity, [10, 37], even in real time, closed-loop settings [56, 74].”
Withdrawn: Large Language Models in Healthcare Decision Support: A ReviewPreprint
Vavekanand, R., Karttunen, P., Xu, Y., Milani, S., & Li, H. (2024) · Preprints.org · doi:10.20944/preprints202407.1842.v1 · Cited by 7
Representation of verbal thought in motor cortex and implications for speech neuroprosthesesPreprint
Kunz, E. M., Meschede-Krasa, B., Kamdar, F. B., Avansino, D. T., Nason, S. R., Card, N. S., Jacques, B. G., Bechefsky, P. H., Hahn, N. V., Iacobacci, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.04.616375 · Cited by 8
Self-citationFedEEG: Federated EEG Decoding Via inter-Subject Structure Matching
Hang, W., Li, J., Liang, S., et al. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095564 · Cited by 7
“It has been received extensive attentions for its potential applications in the brain-computer interfaces (BCIs) [1].”
BackgroundZhang, D., Shafiq, M., Tang, K., & Naseem, U. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3318419 · Cited by 8
“Remarkableprogresshasbeenmade inwirelesscommunicationandinformationprocessingtech-nologies in recent years, leading to the emergence of versatile embedded nodes that sense and communicate wirelessly to form IoT networks [1].”
BackgroundWeng, G., Clark, K., Akbarian, A., Noudoost, B., & Nategh, N. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1273053 · Cited by 8
Neurorights in question: rethinking the concept of mental integrity
Blumenthal‐Barby, J., & Ubel, P. A. (2024) · Journal of Medical Ethics · doi:10.1136/jme-2023-109683 · Cited by 7
A Wireless Bi-Directional Brain–Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission
Ji, W., Su, H., Jin, S., Tian, Y., Li, G., Yang, Y., Li, J., Zhou, Z., Wei, X., Tao, T. H., et al. (2024) · Micromachines · doi:10.3390/mi15111283 · Cited by 9
The expanding repertoire of brain–computer interfaces
Ramsey, N. F., & Vansteensel, M. J. (2025) · Nature Medicine · doi:10.1038/s41591-024-03440-6 · Cited by 7
Combining SNNs with filtering for efficient neural decoding in implantable brain-machine interfaces
Zhou, B., Sun, P.-S. V., & Basu, A. (2025) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/adba82 · Cited by 12
“While the first applications were in motor prostheses to control a cursor on a computer screen [2], or wheelchairs [3], or robotic arms [4], recent studies have shown remarkable results for speech decoding [5, 6], handwritten text generation [7] and therapies for other mental disorders [8].”
SEE: Semantically Aligned EEG-to-Text Translation
Tao, Y., Liang, Y. F., Wang, L., Li, Y., Yang, Q., & Zhang, H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889658 · Cited by 11
Highly Stable Polymeric Electrooculography Electrodes for Contactless Human-Machine Interactions
Yu, X., Luo, Z., Ouyang, X., et al. (2025) · ACS Sensors · doi:10.1021/acssensors.5c00031 · Cited by 7
Sleuthing subjectivity: a review of covert measures of consciousness
Kronemer, S. I., Bandettini, P. A., & González-Castillo, J. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00934-1 · Cited by 7
“…and reconstructs conscious content, such as visual conscious perception during imagery and dreams 45–48 , covert movements (for example, imagined handwriting) 49 , sensory stimulation (for example, sounds and images) 50–53 , and illusory, bistable and hallucinatory conscious perception 54,55 .”
Wang, R., Sui, J., Chen, P., Pedraza, Z. S., Bontekoe, J., Kim, J., Li, P. W. F., Pan, F., Schmuck, E. G., Osaki, S., et al. (2025) · Advanced Materials · doi:10.1002/adma.202510140 · Cited by 8
Gesture encoding in human left precentral gyrus neuronal ensembles
Vargas-Irwin, C. E., Hosman, T., Gusman, J. T., Pun, T. K., Simeral, J. D., Singer-Clark, T., Kapitonava, A., Nicolas, C., Shah, N. P., Avansino, D. T., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08557-z · Cited by 7
“…over communication devices such as tablet computers using continuous 2D ‘ point and click ’ control 18 – 20 , decoding attempted handwriting movements and converting them to text in real time 21 , and even controlling a quadcopter with continuously decoded digit movements over 4 dimensions 22 .”
Self-citationA Bi-LSTM Based Network with Attention Mechanism for EEG Visual Classification
Mo, L., Wang, Y., Zhou, W., Shen, X., & Kong, W. (2021) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus52573.2021.9641318 · Cited by 9
“The communication between people is not only in voice or text, but also through thought [35].”
BackgroundPower-efficient in vivo brain-machine interfaces via brain-state estimation
Valencia, D., Leone, G., Keller, N., Mercier, P. P., & Alimohammad, A. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acb385 · Cited by 7
Nason, S. R., Mender, M. J., Kennedy, E., Lambrecht, J. M., Kilgore, K. L., Chiravuri, S., Kumar, N. G., Kung, T. A., Willsey, M. S., Chestek, C. A., et al. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/accf36 · Cited by 10
Fried-Oken, M. B., Kinsella, M., Stevens, I., & Klein, E. P. (2023) · Brain-Computer Interfaces · doi:10.1080/2326263x.2023.2283345 · Cited by 7
Iterative alignment discovery of speech-associated neural activity
Rabbani, Q., Shah, S., Milsap, G., Fifer, M. S., Heřmanský, H., & Crone, N. E. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad663c · Cited by 6
Implanted cortical neuroprosthetics for speech and movement restoration
Muirhead, W. R., Horsfall, H. L., Aicardi, C., Carolan, J., Akram, H., Vanhoestenberghe, A., Schaefer, A. T., & Marcus, H. J. (2024) · Journal of Neurology · doi:10.1007/s00415-024-12604-w · Cited by 9
“The consortium has made significant contributions to clinical neuroscience with research into motor, speech, sleep, and communication via neuroprosthe-sis, and has progressed engineering aspects such as system development, wireless transmission, and signal processing [12, 14, 50, 51].”
Decoding Finger Velocity from Cortical Spike Trains with Recurrent Spiking Neural Networks
Liu, T., Gygax, J., Rossbroich, J., Chua, Y., Zhang, S., & Zenke, F. (2024) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas61083.2024.10798222 · Cited by 10
Zhou, Y., Chen, W., Li, L., Gong, L., & Zhang, T. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3137837 · Cited by 7
“RAIN-COMPUTER interaction (BCI) is the significant control science and technology that occur in our brain and it is attracting substantial attention from researchers [1-3].”
BackgroundMethodInfluentialDecoding cognition in real-time
Elston, T. W., & Wallis, J. D. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.08.005 · Cited by 7
Robust Online Multiband Drift Estimation in Electrophysiology Data
Windolf, C., Paulk, A. C., Kfir, Y., Trautmann, E. M., Garcia, S., Meszéna, D., Muñoz, W., Hardstone, R., Caprara, I., Jamali, M., et al. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095487 · Cited by 11
The impact of task context on predicting finger movements in a brain-machine interface
Mender, M. J., Nason, S. R., Temmar, H., Costello, J. T., Wallace, D. M., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., & Chestek, C. A. (2023) · eLife · doi:10.7554/elife.82598 · Cited by 7
Brain Driving: Personalizing Vehicle Speed With DR-EEG Decoding and Situational Embeddings
Li, P., Qi, G., Yan, X., Zhao, S., Hu, L., He, Z., & Guan, W. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3430952 · Cited by 7
“Previousworkonsynthesizing speech [23] and text [24] by decoding neural activity has proven the effectiveness of the research paradigm.”
Effects of Gaze Fixation on the Performance of a Motor Imagery-Based Brain-Computer Interface
Meng, J., Wu, Z., Li, S., & Zhu, X. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.773603 · Cited by 7
“It has opened doors to improving the life quality of patients who suffer from neurological disorders such as spinal cord injury and amyotrophic lateral sclerosis (Bouton et al., 2016; Soekadar et al., 2016; Chaudhary et al., 2017; Moses et al., 2021; Willett et al., 2021).”
BackgroundSynthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder FrameworkPreprint
Herff, C. (2021) · arXiv (Cornell University) · Cited by 7
Metadata
- Authors
- Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M., & Shenoy, K. V.
- Affiliations
- Howard Hughes Medical Institute (US)
- Harvard University (US)
- Stanford Medicine (US)
- Corresponding author
- Willett, Francis R.
- Venue
- Nature
- Published
- 2021-05-12
- Volume
- 593
- Issue
- 7858
- Pages
- 249-254
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8163299
- OpenAlex
- W3161343553
- Semantic Scholar
- bebaab40b42e63cd7cef7b334e5f6bacdcccfc79
- Cited by
- 1,024
- References
- 50
- Citations by year
- 2026: 187 · 2025: 225 · 2024: 203 · 2023: 203 · 2022: 134 · 2021: 67
- FWCI
- 70.16
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- public-domain
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, public-domain)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- U.S. Department of Veterans Affairs (I01 RX002295, I50RX002864, B6453-R, N-2864-C)
- Massachusetts General Hospital
- Office of Research and Development (N2864C, B6453R)
- National Institutes of Health (5uh2ns095548-02, 5u01dc017844-06)
- Wu Tsai Neurosciences Institute, Stanford University
- National Institute on Deafness and Other Communication Disorders (R01DC009899, DC017844, R01DC014034, U01- DC017844)
- National Institute of Neurological Disorders and Stroke (UH2-NS095548, U01NS098968, NS095548)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- brain-computer interface, handwriting, text entry, motor cortex, intracortical recording, spinal cord injury, paralysis, real-time communication, typing speed
- MeSH terms
- Brain, Communication, Handwriting, Humans, Spinal Cord Injuries, Time Factors, Neural Networks, Computer, Brain-Computer Interfaces
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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