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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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 401–500 of 967
An emerging view of neural geometry in motor cortex supports high-performance decoding
Perkins, S. M., Amematsro, E. A., Cunningham, J. P., Wang, Q., & Churchland, M. M. (2025) · eLife · doi:10.1101/2023.04.05.535396 · Cited by 12
Jude, J. J., Levi-Aharoni, H., Acosta, A. J., Allcroft, S. B., Nicolas, C., Lacayo, B. E., Card, N. S., Wairagkar, M., Brandman, D. M., Stavisky, S. D., et al. (2025) · medRxiv · doi:10.1101/2025.04.01.25324990 · Cited by 7
“Although iBCI handwriting decoding 16 may provide high throughput communication, many individuals may prefer a keyboard-based communication interface due to its well-established integration with digital communication interfaces and their extensive experience with ten-finger typing.”
InfluentialSelf-citationVarshney, S., Farias, D., Brandman, D. M., Stavisky, S. D., & Miller, L. M. (2023) · 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) · doi:10.1109/ner52421.2023.10123751 · Cited by 6
“Previous intracortical BCIs [1] successfully provided point-and-click and handwriting communication: paralyzed users attempted to move their hand, and algorithms decoded that neural data to make character selections (with speeds of 8 to 19 words per minute).”
BackgroundSarrett, M. E., & Toscano, J. C. (2023) · Psychophysiology · doi:10.1111/psyp.14475 · Cited by 8
Distinct Neuron Types Contribute to Hybrid Auditory Spatial Coding
Chen, C., & Song, S. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.0159-24.2024 · Cited by 7
Branco, M. P., Verberne, M. S. W., van Balen, B., Bekius, A., Leinders, S., Ketelaar, M., Geytenbeek, J., van Driel-Boerrigter, M., Veld, M. W.-O. H., Rabbie-Baauw, K., et al. (2025) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2025.2481426 · Cited by 7
“…progress in the development of cBcis, and promising results in demonstrating cBci-based communication in adults with severe motor impairment [32–36], the pediatric application of this technology has been left mostly untouched [37–39]. this situation provides an excellent opportunity to adopt a…”
Efficient Estimation of Directed Connectivity in Nonlinear and Nonstationary Spiking Neuron Networks
Chen, W., Wang, Y., & Yang, Y. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3319956 · Cited by 5
“Gated recurrent models, as an advanced method for modelling temporal dependencies of neural signals, have been successfully used to decode motor and speech behaviors from neural signals [26], [27].”
MethodDuncan, J. L., Wang, J., Glusauskas, G., Weagraff, G. R., Gao, Y., Hoeferlin, G. F., Hunter, A. H., Hess‐Dunning, A., Ereifej, E. S., & Capadona, J. R. (2024) · Micromachines · doi:10.3390/mi15020286 · Cited by 7
“As a component of brain–machine interfaces (BMIs), IMEs record neural signals that can be translated into control signals for assistive technologies for individuals with disabling neurological conditions such as spinal cord injury or amyotrophic lateral sclerosis (ALS), as well as limb loss [1–4].”
BackgroundProgress in Mechanical Modeling of Implantable Flexible Neural Probes
You, X., Bai, R., Xue, K., Zhang, Z., Wang, M., Wang, X., Wang, J., Guo, J., Shen, Q., Chang, H., et al. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.049047 · Cited by 8
Sun, Y., Chen, X., & Gao, X. (2024) · Journal of Neurorestoratology · doi:10.1016/j.jnrt.2024.100138 · Cited by 6
Chen, Q., Wang, F., Wang, F., Sun, D., & Li, Q. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109615 · Cited by 9
Oxley, T. J. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad9633 · Cited by 6
Building Privacy-Preserving Medical Text Models With a Pretrained Transformer
Shafiq, M., Ren, L., Srivastava, G., Zhang, D., Bourouis, S., & Gadekallu, T. R. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3520426 · Cited by 7
“I N THE information era, the medical field is embracing the growth of big data [1], [2].”
Ho, T. L., Xue, W., See, M. K., Chan, D., Tsang, A. C. O., Mak, C. H., Wong, S., & Lee, M. W. (2025) · Surgical Practice · doi:10.1111/1744-1633.70002 · Cited by 6
“79 The key criteria for clinical applications were now met, including communication being entirely self-paced with the eyes free to move, and sufficient accuracy offline with a large-vocabulary language TABLE 3 Notable developments in BCIs a for motor neuroprostheses.”
Amann, L. K., Casasnovas, V., & Gail, A. (2025) · Nature Communications · doi:10.1038/s41467-025-58738-x · Cited by 6
Towards Predictive Communication: The Fusion of Large Language Models and Brain–Computer Interface
Carìa, A. (2025) · Sensors · doi:10.3390/s25133987 · Cited by 12
“Recent advances include systems capable of translating imagined handwriting into real-time text at speeds up to 90 characters per minute using recurrent neural networks (RNNs) with integrated language modeling and autocorrection [8,10].”
Advancing sensory neuroprosthetics using artificial brain networks
Haslacher, D., Nasr, K., & Soekadar, S. R. (2021) · Patterns · doi:10.1016/j.patter.2021.100304 · Cited by 6
“6 Besides restoration of motor and cognitive functions, neuroprosthetics have also successfully restored sensory function, e.g., in the auditory domain using cochlear implants.”
BackgroundSRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction
Liu, Y., Höllerer, T., & Sra, M. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.803384 · Cited by 6
Brain–Computer Interfaces for Treatment of Focal Dystonia
Simonyan, K., Ehrlich, S. K., Andersen, R. A., Brumberg, J. S., Guenther, F. H., Hallett, M., Howard, M. A., Millán, J. D. R., Reilly, R. B., Schultz, T., et al. (2022) · Movement Disorders · doi:10.1002/mds.29178 · Cited by 6
A Brain-Controlled Mahjong Game with Artificial Intelligence Augmentation
Wu, X., Qi, Y., Zhu, X., Xu, K., Zhu, J., Zhang, J., & Wang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20503-3_47 · Cited by 6
Saalmann, Y. B., Mofakham, S., Mikell, C. B., & Djurić, P. M. (2022) · Current Research in Neurobiology · doi:10.1016/j.crneur.2022.100071 · Cited by 7
“Machine learning has been successfully applied to decoding cortical activity from electrode arrays for neural prostheses (Anumanchipalli et al., 2019; Willett et al., 2021).”
MethodOvert speech decoding from cortical activity: a comparison of different linear methods
Godais, G. L., Roussel, P., Bocquelet, F., Aubert, M., Kahane, P., Chabardès, S., & Yvert, B. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1124065 · Cited by 8
“, 2018) or by reconstructing imagined handwritten letters (Willett et al., 2021).”
BackgroundWireless agents for brain recording and stimulation modalities
Bok, I., Vareberg, A. D., Gokhale, Y., Bhatt, S., Masterson, E., Phillips, J. K., Zhu, T., Ren, X., & Hai, A. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-023-00122-5 · Cited by 6
“…interfaces (BMIs) primarily rely on mainstay electrophysiological tools including microelectrode arrays (Chaudhary et al. 2022; Spira and Hai 2020; Willett et al. 2021), electrocorticography (ECoG) arrays (Merk et al. 2022) and other implantable shanks (Paulk et al. 2022) that often elicit a…”
BackgroundCross-Modal Cloze Task: A New Task to Brain-to-Word Decoding
Zou, S., Wang, S., Zhang, J., & Zong, C. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.54 · Cited by 8
“In recent years, several breakthroughs have been made in this field and demonstrated the feasibility to decode speech (Anumanchipalli et al., 2019; Makin et al., 2020; Moses et al., 2021) or handwriting (Willett et al., 2021) from neural activity at high accuracy and speed.”
BackgroundHardware-Efficient Compression of Neural Multi-Unit Activity
Savolainen, O. W., Zhang, Z., Feng, P., & Constandinou, T. G. (2022) · IEEE Access · doi:10.1109/access.2022.3219441 · Cited by 6
Riemannian Geometric Instance Filtering for Transfer Learning in Brain-Computer Interfaces
Hui, Q., Liu, X., Li, Y., Xu, S., Zhang, S., Sun, Y. Z., Wang, S., Chen, X., & Zheng, D. (2022) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3560905.3568434 · Cited by 7
“Therefore, the MI-BCI systems are generally regarded as the brain-computer interface paradigm closest to the natural interaction patterns and have a wide range of applications, such as communication, rehabilitation, and entertainment [5–8].”
BackgroundSimple decoding of behavior from a complicated neural manifoldPreprint
Perkins, S. M., Cunningham, J. P., Wang, Q., & Churchland, M. M. (2023) · eLife · doi:10.7554/elife.89421.1 · Cited by 6
Reclaiming Hand Functions for Chronic Spinal Cord Injury with Epidural Brain-Computer InterfacePreprint
Liu, D., Shan, Y., Chen, L., Li, W., Yao, X., Yao, R., Zheng, J., Liang, F., Zhu, W., Xu, H., et al. (2024) · medRxiv · doi:10.1101/2024.09.05.24313041 · Cited by 10
Direct Speech Reconstruction from Sensorimotor Brain Activity with Optimized Deep Learning Models
Berezutskaya, J., Freudenburg, Z. V., Vansteensel, M. J., Aarnoutse, E. J., Ramsey, N. F., & van Gerven, M. A. J. (2022) · doi:10.1101/2022.08.02.502503 · Cited by 42
Yang, C., Zhang, Z., Zhang, L., Zhang, Y., Li, Z., Luo, Y., Pan, G., & Zhao, B. (2024) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2024.3416728 · Cited by 5
Adaptive channel-weight dual-constrained semi-supervised EEG clustering
Bian, J., Dai, C., & Li, G. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106720 · Cited by 5
Investigating the benefits of artificial neural networks over linear approaches to BMI decoding
Temmar, H., Willsey, M. S., Costello, J. T., Mender, M. J., Cubillos, L. H., DeMatteo, J. C., Lam, J. L., Wallace, D. M., Kelberman, M. M., Patil, P. G., et al. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ade568 · Cited by 11
“As a result, nonlinear models have now been used to significantly outperform linear methods in both offline and real-time BMI decoding tasks [4, 5, 33–36].”
Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain‐to‐Text Communication
Xu, G., Wang, Z., Xu, K., Zhu, J., Zhang, J., Wang, Y., & Hao, Y. (2025) · Advanced Science · doi:10.1002/advs.202505492 · Cited by 9
Silva, A. B., Liu, J. R., Anderson, V. R., Kurtz-Miott, C. M., Hallinan, I. P., Littlejohn, K. T., Brosler, S., Tu-Chan, A., Ganguly, K., Moses, D. A., et al. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adf50e · Cited by 6
“Recent work has demonstrated that neuroprostheses can restore naturalistic communicative ability to people with paralysis by recording brain activity and applying algorithms to decode this activity into intended movements and, thereby, speech [3–12].”
A systematic review of EEG-based Imagined Speech decoding
Jin, Z., Li, D., & Huang, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113563 · Cited by 7
Brain-to-text decoding with context-aware neural representations and large language models
Li, J., Le, T., Fan, C., Chen, M., & Shlizerman, E. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adfab1 · Cited by 9
“These messages can be conveyed through various modalities, including typed characters [1], handwriting [2], text [3–5], and synthesized speech [5].”
Li, W., Zou, H., Yang, B., Xiao, L., Liu, S., Chen, Z., Xie, L., Zhu, W., Zhao, X. Q., Wang, L., et al. (2025) · Small Methods · doi:10.1002/smtd.202501471 · Cited by 5
Sen, O., Soni, R., Virmani, D., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-24972-y · Cited by 5
“Willett et al. used Utah arrays implanted in the motor cortex to decode attempted handwriting, achieving 90 characters per minute with 94.1% accuracy in real time 11 .”
InfluentialSensing technologies for silent speech interfaces
Tang, C., Qi, L., Gao, S., Zhang, Z., Yi, W., Xu, M., Occhipinti, E., Pan, Y., & Occhipinti, L. G. (2026) · Nature Sensors · doi:10.1038/s44460-025-00010-2 · Cited by 8
A mosaic of whole-body representations on the human precentral gyrus
Deo, D. R., Okorokova, E. V., Pritchard, A. L., Hahn, N. V., Card, N. S., Nason, S. R., Jude, J. J., Hosman, T., Choi, E. Y., Qiu, D., et al. (2026) · Nature · doi:10.1038/s41586-026-10653-x · Cited by 21
“This library has been extensively used in our previous reports 37,41,56,64 (see the code repository on GitHub (https://github.com/ fwillett/cvVectorStats)).”
Self-citationMulti-channel LFP recording data compression scheme using Cooperative PCA and Kalman Filter
Wang, C., Yin, M., Yu, S., & Wang, X. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105468 · Cited by 5
Bio‐p–n Junction–Derived Bio–Diodes With Opto‐Ionotronic Switching and Rectification Effects
Lv, Y., Xie, S., Huang, J., Lu, S., Luo, D., & Xiang, Y. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202407744 · Cited by 5
Brain-Computer Interface for Patients with Spinal Cord Injury: A Bibliometric Study
Feng, J., Gao, S., Hu, Y., Sun, G., & Sheng, W. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.08.163 · Cited by 5
Ma, X., Rizzoglio, F., Bodkin, K. L., & Miller, L. E. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adab93 · Cited by 6
“An intracortical brain computer interface (iBCI), which converts the spiking activity of simultaneously recorded motor cortical neurons into user intentions, is a promising tool for assisting individuals suffering from paralysis or other neuromotor disabilities [1–4].”
A Neuroscience-Informed AI Framework to Decode the Complexities of Neurofinance
Bormpotsis, C., Nanos, M., & Patel, A. (2025) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2025.3556355 · Cited by 5
“Studies furthered this effort by developing an intracortical BCI to decode handwriting attempts from neural signals, enabling participants with spinal cord injuries to type at speeds comparable to smartphone typing [48].”
Serebryakov, M. Y., & Moiseev, I. S. (2022) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/elconrus54750.2022.9755545 · Cited by 5
Saeki, S., Tokutake, K., Takasu, M., Kurimoto, S., Asami, Y., Onaka, K., Saeki, M., & Hirata, H. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23158773 · Cited by 4
Reichert, C., Klemm, L., Mushunuri, R. V., Kalyani, A., Schreiber, S., Kuehn, E., & Azañón, E. (2022) · Sensors · doi:10.3390/s22166101 · Cited by 6
“To achieve this, we applied the DTW algorithm, which has been successfully used in many applications, such as driving pattern recognition [25], gesture recognition [26], and aligning motor neural activity [27].”
MethodPartially binarized neural networks for efficient spike sorting
Valencia, D., & Alimohammad, A. (2022) · Biomedical Engineering Letters · doi:10.1007/s13534-022-00255-7 · Cited by 5
“For example, in [1] neural activity associated with imagined handwriting is used to convert the brain’s activity into text.”
MethodElectronic Neurons for a New Learning Paradigm
Wang, Z., Wang, J., Shi, X., Zhu, Z., Chen, P., & Peng, H. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202203247 · Cited by 5
Decoding Imagined Musical Pitch From Human Scalp Electroencephalograms
Chung, M., Kim, T.-H., Jeong, E., Chung, C. K., Kim, J. S., Kwon, O., & Kim, S.-P. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3270175 · Cited by 7
Groth, O. J., Esposito, M., & Tse, T. C. M. (2023) · The MIT Press eBooks · doi:10.7551/mitpress/14360.001.0001 · Cited by 5
Elgohary, T., & Aljohani, M. M. (2023) · Journal of the Pakistan Medical Association · doi:10.47391/jpma.7712 · Cited by 5
Automatic control of reactive brain computer interfaces
Tufvesson, P., & Heskebeck, F. (2024) · IFAC Journal of Systems and Control · doi:10.1016/j.ifacsc.2024.100251 · Cited by 6
AI for brain-computer interfaces
Haslacher, D., Akmazoglu, T. B., van Beinum, A., Starke, G., Buthut, M., & Soekadar, S. R. (2024) · Developments in neuroethics and bioethics · doi:10.1016/bs.dnb.2024.02.003 · Cited by 8
Neural Decoding of Spontaneous Overt and Intended Speech
Dash, D., Ferrari, P., & Wang, J. (2024) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-24-00046 · Cited by 7
“Recent improvements using imagining handwriting of letters (Willett et al., 2021) or imagining letters directly further improved the communication rate to about 15 words per minute (Moses et al., 2021).”
Kernel-Based Actor–Critic Learning Framework for Autonomous Brain Control on Trajectory
Song, Z., Zhang, X., Chen, S., Tan, J., & Wang, Y. (2024) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2024.3485078 · Cited by 5
“B RAIN – MACHINE interfaces (BMIs) [1], [2] aim to restore disabled motor functions for paralyzed people by translating neural signals into control commands and interacting with external devices, such as cursors [3], [4] and robotic arms [5], [6].”
Tan, J., Zhang, X., Wu, S., Song, Z., & Wang, Y. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3503713 · Cited by 6
“B RAIN-MACHINE interfaces (BMIs) have emerged as a promising approach for restoring lost function for individuals with paralysis [1], [2].”
Ten ignored questions for stress psychology research.
Gan, Y.-Q., Ma, J.-J., Peng, H., Zhu, H., Ju, Q., & Chen, Y.-D. (2022) · PsyCh Journal · doi:10.1002/pchj.520 · Cited by 6
Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis
Wu, J., Wang, Z., Xu, T., & Sun, C. (2022) · Sensors · doi:10.3390/s22155631 · Cited by 5
“Depending on the recording devices being invasive or noninvasive, BCIs can also be divided into invasive [4,5] and noninvasive ones [1,2].”
BackgroundApplication of Kurtosis Based Dynamic Window to Enhance SSVEP Recognition
Yin, H., Ji, Z., Lian, Z., et al. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055430 · Cited by 5
“It does not rely on peripheral nerves of the brain and muscles, but directly controls external devices by EEG to realize the communication between the human brain and machine [1]–[3].”
BackgroundSwin-TCNet: Swin-based temporal-channel cascade network for motor imagery iEEG signal recognition
Xu, M., Zhou, W., Shen, X., Wang, Y., Mo, L., & Qiu, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104885 · Cited by 5
Subject Generalization in Classifying Imagined and Spoken Speech with MEG
Dash, D., Ferrari, P., Abbas Babajani-feremi, A., Harwath, D. F., Borna, A., & Wang, J. (2023) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner52421.2023.10123722 · Cited by 6
“Although recent advances based on imagined handwriting obtained up to 90 characters per minute [2], it is still significantly below the normal speaking rate (i.”
BackgroundExploratory methods for high-performance EEG speech decodingPreprint
Comstock, L. B., Lainscsek, C. S. M., Carvalho, V. R., Mendes, E. M. A. M., Fallah, A., & B, T. J. S. A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.16.468876 · Cited by 4
“In recent years, the field of speech decoding has made significant advances that place the goal of real-time translation from neural signals within sight [28, 44].”
BackgroundMishra, A. R., Kumar, R., Gupta, V., Prabhu, S. M., Upadhyay, R., Chhipa, P. C., Rakesh, S., Mokayed, H., Chakladar, D. D., De, K., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03546-z · Cited by 10
“In 35 , authors used an intracortical Brain-Computer Interface (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 This is a performance analysis on the data of Task-4, i.e., EEG…”
MethodLeinders, S., Aarnoutse, E. J., Branco, M. P., Freudenburg, Z. V., Geukes, S. H., Schippers, A., Verberne, M. S. W., van den Boom, M., van der Vijgh, B. H., Crone, N. E., et al. (2024) · medRxiv · doi:10.1101/2024.10.11.24315027 · Cited by 5
“…that people with severe paralysis can use an implanted BCI for communication (Hochberg et al., 2006; Metzger et al., 2023, 2022; Mitchell et al., 2023; Moses et al., 2021; Oxley et al., 2021; Pandarinath et al., 2017; Stavisky et al., 2018; Vansteensel et al., 2016; Willett et al., 2023, 2021).”
BackgroundMethodBrain–Computer Interfaces in Spinal Cord Injury: A Promising Therapeutic Strategy
Li, S., Gao, S., Hu, Y., et al. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.70183 · Cited by 4
“(Willett et al. 2021).”
Huff, J. R. T., Wang, J., Gao, Y., Yayci, Z. E., Chan, E. R., Hess‐Dunning, A., & Capadona, J. R. (2025) · Biomaterials · doi:10.1016/j.biomaterials.2025.123692 · Cited by 6
The Black Box Paradox: AI Models and the Epistemological Crisis in Motor Control Research
Dias, N., Pinho, L., Silva, S., Freitas, M., Figueira, V., & Pinho, F. (2025) · Information · doi:10.3390/info16100823 · Cited by 4
Ehrlich, S. K., Tougas, G., Bernstein, J., Buie, N. E., Rumbach, A. F., & Simonyan, K. (2025) · Movement Disorders · doi:10.1002/mds.70114 · Cited by 4
Luo, Y., Liu, X., & Yang, M. (2026) · Frontiers in Rehabilitation Sciences · doi:10.3389/fresc.2026.1666530 · Cited by 4
Individualized brain-computer interface for people with disabilities: a review
Saha, S., Karlsson, P., Anderson, C. J., Kavehei, O., & McEwan, A. L. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1738876 · Cited by 5
“…brain (Card et al., 2024; Willett et al., 2023; Huggins et al., 2022; Karlsson et al., 2022; Orlandi et al., 2021; Mrachacz-Kersting et al., 2021; Willett et al., 2021; Saha et al., 2021; Moses et al., 2021; Flesher et al., 2021; Mrachacz-Kersting et al., 2016; Rao et al., 2014; Moghimi et al.,…”
Restoring rapid natural bimanual typing with a neuroprosthesis after paralysis
Jude, J. J., Levi-Aharoni, H., Acosta, A. J., Allcroft, S., Nicolas, C., Lacayo, B. E., Card, N. S., Wairagkar, M., Levin, A. D., Brandman, D. M., et al. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02218-y · Cited by 13
“Looking more closely at the effect of electrode count on decoding performance, we examined both iBCI handwriting 16 and typing decoding accuracy with T18 as a function of electrode count.”
InfluentialSelf-citationJude, J. J., Haro, S., Levi-Aharoni, H., Hashimoto, H., Acosta, A. J., Card, N. S., Wairagkar, M., Brandman, D. M., Stavisky, S. D., Williams, Z. M., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117162 · Cited by 13
“More recently, iBCIs and electrocorticography (ECoG)-based BCI systems have been shown to be effective at higher throughput communication, for example, through articulator-based speech decoding, 13–26 character-based handwriting decoding, 27,28 and finger-based typing.”
Self-citationNoninvasive decoding of typed sentences from human brain activity
Levy, J., Zhang, M., Pinet, S., Rapin, J., Banville, H. J., d’Ascoli, S., & King, J.-R. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02303-2 · Cited by 49
“Indeed, while invasive studies have successfully transferred decoding models from execution to ‘attempted’ movement 9 , validating this transfer with noninvasive signals remains a necessary next step.”
Pan, H., Zhang, Y., Li, L., & Qin, X. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.12.001 · Cited by 4
Li, S., Li, Y., & Zhang, R. (2024) · Brain‐X · doi:10.1002/brx2.70001 · Cited by 4
Neurosurgery and the Brain-Computer Interface
Rosenfeld, J. V. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-64892-2_32 · Cited by 5
Deciphering the skeletal interoceptive circuitry to control bone homeostasis
Wu, Y., Guo, J., Chen, Z., Zhang, F., Chow, B., Chen, Z., Yeung, K., & Qiao, W. (2025) · BMEMat · doi:10.1002/bmm2.12138 · Cited by 6
Huang, S., & Wei, Q. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110691 · Cited by 4
Progress, challenges and future of linguistic neural decoding with deep learning
Wang, Y., Liu, H., Wang, Y., Xuan, C., Hou, Y., Feng, S., Liu, H., Liao, Y., & Wang, Y. (2025) · Communications Biology · doi:10.1038/s42003-025-08511-z · Cited by 10
“Manypatientsrelyonabrain – computer interface (BCI) to spell words 90,91 , move the computer cursor 92 , and direct handwriting 93 .”
Akhoundi, A., Yan, P., Landbrug, Y., Hays, M. R., Murmann, B., Chichilnisky, E. J., & Muratore, D. G. (2025) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2025.3611139 · Cited by 4
“motor and speech intentions in humans for several therapeutic applications [1], [2], [3], [4], [5].”
Intracortical brain-computer interfaces for improved motor function: a systematic review
Holt, M. W., Robinson, E. C., Shlobin, N. A., Hanson, J. T., & Bozkurt, İ. (2023) · Reviews in the Neurosciences · doi:10.1515/revneuro-2023-0077 · Cited by 6
Highly Generalizable Spelling Using a Silent-Speech BCI in a Person with Severe Anarthria
Metzger, S. L., Liu, J. R., Moses, D. A., Dougherty, M. E., Seaton, M. P., Littlejohn, K. T., Chartier, J., Anumanchipalli, G. K., Tu-Chan, A., Ganguly, K., et al. (2023) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-031-49457-4_3 · Cited by 5
Recruiting neural field theory for data augmentation in a motor imagery brain–computer interface
Polyakov, D., Robinson, P. A., Müller, E. J., & Shriki, O. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1362735 · Cited by 4
“…hold particular significance for individuals unable to utilize traditional communication methods, who find themselves entirely locked in due to conditions such as amyotrophic lateral sclerosis, stroke, or traumatic brain injury (Artzi and Shriki, 2018; Nguyen et al., 2018; Willett et al., 2021).”
BackgroundIkegawa, Y., Fukuma, R., Sugano, H., Oshino, S., Tani, N., Tamura, K., Iimura, Y., Suzuki, H., Yamamoto, S., Fujita, Y., et al. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad417a · Cited by 4
“Recently, a combination of deep learning and natural language processing (NLP) significantly improved the speed and accuracy of text generated using the iEEG of paralyzed patients [6, 7].”
BackgroundMethodA Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy
Jiang, X., Ou, L. S., Chen, Y., Ao, N., Chang, Y., Do, T., & Lin, C. (2025) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2025.3528376 · Cited by 4
15.1 A 3.9mW 200words/min Neural Signal Processor in Speech Decoding for Brain-Machine Interface
Chang, T.-Y., Wang, J., Tsai, Y.-H., et al. (2025) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc49661.2025.10904671 · Cited by 4
“A handwriting-based BMI [2] converts attempted handwriting movements to characters, but the communication rate is low for natural speech.”
Glannon, W. (2021) · doi:10.1093/oso/9780198813910.001.0001 · Cited by 4
Next-generation BCIs: Brain-to-text Communication via Attempted Handwriting
Willett, F. R., Avansino, D. T., Hochberg, L. R., et al. (2022) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci53720.2022.9735049 · Cited by 4
Self-citationThe future is here: Mind control and torture in the digital era
Pérez‐Sales, P. (2022) · Torture Journal · doi:10.7146/torture.v32i1-2.132846 · Cited by 4
“Different Thought-to-Text devices are already available (Willett et al., 2021) https://store.neurosky.com/ be marketed.”
BackgroundSlow Firing Single Units Are Essential for Optimal Decoding of Silent Speech
Kennedy, P., Ganesh, A., & Cervantes, A. J. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.874199 · Cited by 6
Flexible Bi-directional Brain Computer Interface for Controlling Turning Behavior of Mice
Ye, Y., Tian, Y., Wang, H., Cheng, Q., Zhang, K., Wang, X., Zhou, C., Xu, C., Wei, X., Zhou, Z., et al. (2023) · IEEE International Conference on Micro Electro Mechanical Systems (MEMS) · doi:10.1109/mems49605.2023.10052363 · Cited by 5
“Most invasive BCI systems focus on neural signal acquisition for improved performance on the control of external devices or sensory recognition [2, 3].”
BackgroundCarlessi, M. M., Borges, G. S., & Calgaro, C. (2023) · Revista de Direito Brasileira · doi:10.26668/indexlawjournals/2358-1352/2022.v32i12.8502 · Cited by 4
Sun, H., Li, C., & Zhang, H. (2024) · IEEE Access · doi:10.1109/access.2024.3368384 · Cited by 4
“Willett et al. aided users with disabilities in completing imagery writing [23].”
BackgroundBridging Medicine and Technology for Advancing Patients Care
Karami, M. M. (2024) · Pharmacophore · doi:10.51847/eslcv4vhgl · Cited by 7
Ryu, J., Choi, J. W., Niketeghad, S., Torres, E. B., & Pouratian, N. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad2e1d · Cited by 4
Temporal-spatial cross attention network for recognizing imagined characters
Xu, M., Zhou, W., Shen, X., Qiu, J., & Li, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-59263-5 · Cited by 5
“To evaluate the effectiveness of our proposed model, we conducted experiments on the Imagery Handwritten Character dataset 14 .”
MethodFarzan, F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95730-4.00003-2 · Cited by 6
Mechanisms of Premotor-Motor Cortex Interactions during Goal Directed BehaviorPreprint
Alyahyay, M., Kalweit, G., Kalweit, G., Karvat, G., Ammer, J. J., Schneider, A., Adžemović, A., Vlachos, A., Boedecker, J., & Diester, I. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.20.524944 · 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 ›
Are you an author, or do you see a problem with this page? Request a review
