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.
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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.
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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.
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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.
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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 801–900 of 967
Flexible Strain Sensors With Multifusion Algorithm Models for Handwriting Recognition
Zhou, X., Wang, W., Hui, Y., Li, X., Yan, X., & Cheng, T. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3540598
“H ANDWRITING recognition plays a pivotal role in artificial intelligence, particularly in today’s highly digitized world where it garners considerable attention [1], [2], [3], [4].”
Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design
Shawki, N., Napoli, A., Vargas-Irwin, C. E., Thompson, C. K., Donoghue, J. P., & Serruya, M. D. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1544397 · Cited by 2
Visible coalitions of neuronal activities in brain-to-text communication via handwriting
Bolognino, J., Cohen, S., Bar, E., et al. (2025) · doi:10.1117/12.3056602
“Here, we applied a CNN to data recorded by [8] while a human subject was engaged in imagining writing English characters (see details of the experiments in [8]).”
Comparing a BCI communication system in a patient with Multiple System Atrophy, with an animal model
Premchand, B., Toe, K. K., Wang, C., Wan, K. R., Selvaratnam, T., Toh, V. E., Ng, W. H., Libedinsky, C., Chen, W., Lim, R., et al. (2025) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2025.111289
“…a bypass of Sherrington’s final common path [18], [19]. o A machine learning/ML (also sometimes known as artificial intelligence/AI) model is used to then used to convert these signals into a form understandable by humans, such as binary “yes/no” outputs, cursor movements, or even text [20], [21].”
The Brain-Inspired Cooperative Shared Control Framework for Brain-Machine Interface
Yang, J., Liu, L., Zheng, S., Qian, L., Gao, G., Chen, X., & Li, X. (2024) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1109/ricai64321.2024.10911194 · Cited by 1
Noninvasive BCI for Communication
Chaudhary, U. (2025) · Springer eBooks · doi:10.1007/978-3-031-78991-5_4
Zhang, Z., Huang, Z., Yang, L., Li, J., Zheng, Y., & Li, Z. (2025) · IEEE International Conference on Micro Electro Mechanical Systems (MEMS) · doi:10.1109/mems61431.2025.10917449
Chang, E. F., & Chung, J. E. (2025) · The Transmitter · doi:10.53053/gifk3905
Partial Domain Adaptation for Stable Neural Decoding in Disentangled Latent Subspaces
Wang, P., Qi, Y., & Pan, G. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3577222 · Cited by 1
“B RAIN-COMPUTER Interfaces (BCI) offer a direct pathway to connect the brain with external devices, demonstrating great potential in neural rehabilitation for people with paralysis [1], [2], [3], [4], [5].”
InfiniMind: A Learning-Optimized Large-Scale Brain-Computer Interface
Jang, Y., Jung, D., Song, S., Lee, H., & Kim, J. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731067
“Some studies emphasize the need for online learning to address sudden and frequent non-stationarities [27, 35], while others adopt a more relaxed timescale (e.g., daily) [76, 120].”
InfluentialWang, X., Cai, X., Fu, L., et al. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-94266-2_15
Wang, T., & Chen, J. (2025) · doi:10.1109/cisce65916.2025.11065011
“A Brain-Computer Interface (BCI) establishes a direct communication pathway between the human brain and external devices by translating biological signals, such as electroencephalography (EEG), into actionable commands [1].”
Weng, J. (2025) · Proceedings of the 2025 International Conference on Artificial Intelligence and Computational Intelligence · doi:10.1145/3730436.3745779
Drug self-administration, drug-specific behaviors, and neurobehavioral deep learning analyses
Beacher, N. J., Wang, M. W., Iringan, J. S. J., Clark, C. E., D’Ottavio, G., Broomer, M. C., & Lin, D. (2025) · Addiction Neuroscience · doi:10.1016/j.addicn.2025.100222 · Cited by 1
Yoshimura, N. (2025) · Koutou (THE LARYNX JAPAN) · doi:10.5426/larynx.37.20
Restoring communication with speech brain–computer interfaces
Prakash, P. R., Luo, S., Crone, N. E., & Slutzky, M. W. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24824-5.00015-6 · Cited by 2
HWI encoding/decoding of a non-invasive HWI—BCI paradigm based on temporal variation abundance scale
Ding, P., Wang, F., Zhao, L., Gong, A., & Fu, Y. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10274-6
“…input, provides an optional solution for individuals with severe motor disorders and other reasons that result in loss of handwriting function, or for healthy individuals who find it inconvenient or are unable to write in specific scenarios (Chen et al. 2015; Mao et al. 2021; Willett et al. 2021).”
InfluentialEthics in Rehabilitation Psychology
Hanson, S. L., & Kerkhoff, T. R. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197525456.013.0057
Wu, X., Hu, K., Fu, Z., & Zhang, D. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.146 · Cited by 1
“Almost all studies would first decode speech features, such as spectrograms ( Angrick et al., 2019; Metzger et al., 2023; Willett et al., 2021), before converting them into speech waveforms using algorithms such as Griffin-Lim ( Verwoert et al., 2022; Wu et al., 2024) or deep learning-based methods…”
Exploring Synergies in Brain-Machine Interfaces: Compression vs. PerformancePreprint
Cubillos, L. H., Kelberman, M. M., Mender, M. J., Hite, A., Temmar, H., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., Chestek, C. A., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5414881
Exploring Synergies in Brain-Machine Interfaces: Compression vs. PerformancePreprint
Cubillos, L. H., Kelberman, M. M., Mender, M. J., Hite, A., Temmar, H., Willsey, M. S., Kumar, N. G., Kung, T. A., Patil, P. G., Chestek, C. A., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5414880
Exploring the Wonders of the Brain by Directly Connecting it to a Computer
Vaadia, E. (2025) · Frontiers for Young Minds · doi:10.3389/frym.2025.1577785
“These BCIs can read a person’s neuronal activity while they are thinking about a sentence, and can then display the sentence on a screen for the person (and others) to read (Figure 3) [4].”
Iwata, T., Nakamura, H., Uemura, T., Araki, T., Matsumura, T., Abe, T., Nezu, T., Takagaki, M., Ozaki, T., Miura, S., et al. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202500487 · Cited by 2
Al-Quraishi, M. S., Ahmed, G., AbouOmar, M. S., Ouerdane, F., Eltayeb, A., Alyazidi, N. M., & Ali, S. S. A. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10616-w
“In particular, these models enable the way for revolutionary applications in industry and healthcare by facilitating direct communication between the brain and external devices [5–7].”
Synthetica: The First AI-Native Cohort and the Democratic Risks of Synthetic InfrastructuresPreprint
Milelli, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5450674
Nekoui, M., & Sodagar, A. M. (2025) · Communications Engineering · doi:10.1038/s44172-025-00504-4
“Real-time operation of the proposed spike compressor is of crucial importance in applications such as prosthetic devices and brain-machine interfaces 30,31 .”
Mind-Sketch: A Single-Channel, Non-Invasive Brain-Computer Interface for Writing and Drawing
Banerjee, S., Unni, A., Goveas, N., et al. (2025) · doi:10.1109/icufn65838.2025.11170101
Silva, L., Atencio, A. C., & Delisle-Rodríguez, D. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-94934-0_81
Joint temporal-frequency-channel attention learning for EEG-based visual object classification
Zhou, W., Qu, N., Yang, C., Li, Y., Mo, L., Lin, L., & Dai, G. (2025) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2025.108128
Artificial Intelligence at the Somatic Precipice
Erickson, J., & Johnson, D. (2025) · Springer eBooks · doi:10.1007/978-3-031-94105-4_7
Towards decoding inner speech from EEG and MEGPreprint
Csáky, R., van Es, M. W. J., Jones, Ō. P., & Woolrich, M. W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.13.682161
“In a different line of work, Willett et al. (2021) demonstrate the potential for real-time decoding of attempted handwriting movements from neural activity as a high-speed BCI.”
Sasaki, H., Shimizu, S., Nerchan, R. A., Yamashita, K., Takahashi, N., Chinone, R., Kurashige, Y., Numano, R., Koida, K., Ohsawa, M., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5605796
Research on optimization of brain-controlled typing recognition based on imaginary handwriting
Nan, J., Xu, P., Zhang, K., Xu, Q., Zhang, S., Xia, Y., Han, C., Li, D., & Meng, Y. (2025) · Cluster Computing · doi:10.1007/s10586-025-05761-8
EEG-Driven Image Reconstruction with Saliency-Guided Diffusion Models
Abramov, I. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754476
“While fMRI-based stimulus reconstruction shows promise [6, 26], EEG offers greater practicality through portability and temporal resolution [27].”
A High-Density Silicon-Based Microelectrode Array for Chronic In Vivo Neural Recording
Geng, L., Wang, Y., Gu, W., Yang, Y., Xie, D., Ma, X., Chen, Z., Xu, L., Li, C., & Wu, F. H. (2025) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2025.3621151 · Cited by 1
“On the other hand, in applications requiring high spatial resolution and high channel density [12], [24], silicon-based electrodes remain an attractive option, as they can integrate hundreds to thousands of recording sites with precise geometric arrangements [25], and allow the integration of…”
Zero-Shot Speech Perception Decoding via Advancing Representation Consistency
Xiao, Y., Qiao, X., Zhang, Y.-X., & Yu, X. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209914
“Achieving generalized zero-shot speech perception decoding using non-invasive neural activity signals holds significant importance in the fields of brain-computer interfaces (BCI) [1] and cognitive neuroscience [2].”
Generative motor imagery dynamic networks: EEG-controlled grasping via individualized model training
Wu, X., Long, D., & Yang, J. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10360-9
“…patients to walk (Donati et al. 2016), decoding produced and perceived utterances through electrocortico-gram (ECoG) signals (Moses et al. 2019), and intracortical BCI that decodes attempted handwriting movements from the motor cortex and translates it to text in real time (Willett et al. 2021 ).”
Sasaki, H., Shimizu, S., Nerchan, R. A., Yamashita, K., Takahashi, N., Chinone, R., Kurashige, Y., Numano, R., Koida, K., Ohsawa, M., et al. (2025) · Sensing and Bio-Sensing Research · doi:10.1016/j.sbsr.2025.100915 · Cited by 1
A 40-nm 3.9mW, 200words/Min Neural Signal Processor in Speech Decoding for Brain-Machine Interface
Chang, T.-Y., Wang, J.-B., Tsai, Y.-H., Tsao, Y., & Yang, C. (2025) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2025.3625650
“Handwriting-based BMI [9] converts an attempted handwriting movement to a character.”
Decoding Thoughts, Encoding Ethics
Rudroff, T. (2025) · Neurocultural health and wellbeing · doi:10.1007/978-3-032-11596-6_5
Foundations of AI in Modern Neuroscience
Rudroff, T. (2025) · Neurocultural health and wellbeing · doi:10.1007/978-3-032-11596-6_1
Aithal, P. S., Saldanha, D., Satpathy, J., & Sandhya, S., V (2025) · Poornaprajna International Journal of Emerging Technologies (PIJET) · doi:10.64818/pijet.3107.8486.0015
jianqiu, W., Yang, B., Chen, X., et al. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae2512
Pivotal Moments in the Development of Modern Neurosurgery
Hartley, B. R., Stieg, P. E., & Apuzzo, M. L. J. (2025) · Neurosurgery · doi:10.1017/9781009597852.004
Causality-inspired brain-visual contrastive learning for zero-shot visual decoding
Xiao, Y., Qiao, X., Zhang, Y.-X., & Yu, X. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116182
Sen, O., Khalifa, A., & Chatterjee, B. (2025) · doi:10.1109/embc58623.2025.11253375
“Willett motor cortex (ECoG) signals, decoding 90 CPM with 94.1% raw accuracy and 5.9% CER, while achieving over 99% offline accuracy with a language model [12].”
InfluentialTriangular Network Model for the Neural Basis of Calligraphy
Ding, N. (2025) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.70153
“Limited by the complexity of writing, investigations on the neurophysiological basis of writing have started only recently [8, 9].”
State-Dependent Neural Decoding of Complex Handwriting via Mixture of Experts With Recurrent Routing
Wang, L., Yang, X., Xiong, X., Jia, Y., Wang, Y., & Qi, Y. (2025) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2025.3642409 · Cited by 1
“For example, Willett et al. [3] successfully decoded English letters from multiunit activities in the motor cortex, achieving an impressive accuracy of 94.1%.”
InfluentialJi, M., Meng, J., Li, S., et al. (2025) · International Conference on Biomedical Signal and Image Processing (ICBIP) · doi:10.1109/icbip66535.2025.11284330
“In recent years, significant advancements had been made in the development of BCIs for various applications, including environmental control [2], communication [3], and rehabilitation [4], where BCIs of a robotic arm attracted lots of research interest [5]-[8].”
Understanding the interplay of cognitive factors and STEM achievement: implications for education
Bayaga, A. (2025) · Discover Education · doi:10.1007/s44217-025-01047-8 · Cited by 2
“These issues are key because, as H/BCI technology becomes increasingly prevalent, its potential to tailor educational experiences to individual cognitive profiles presents unique opportunities for enhancing learning, especially in STEM fields [40–42].”
Sun, X., LI, Y. G., Zhai, X., et al. (2025) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-025-01281-0
Adhikary, S., Dutta, S., Bose, A., et al. (2025) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2025.2602830
Automatic Detection of P300 Response Using Spatiotemporal Beamforming TechniquesPreprint
Bohlouli, A. S., & Ahmadi, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5958982
Causality Inspired Brain-Visual Contrastive Learning for Zero-Shot Visual DecodingPreprint
Xiao, Y., Qiao, X., Zhang, Y., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5988659
Surrogate deep neural networks reveal hierarchical handwriting encoding in the human motor cortex
Chen, J., Xu, T., Xiong, X., Yang, X., Wang, Y., & Qi, Y. Y. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116837
Neural Mechanisms of Mixed Speech and Grasp Representation in Sensorimotor CorticesPreprint
Foli, C., Conlan, E. C., Memberg, W. D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.02.697428
Andrade, P., Mercado, R. R. P. S., Jiménez, F., & Visser-Vandewalle, V. E. R. M. (2026) · Die Chirurgie · doi:10.1007/s00104-025-02436-3
Su, H., Hu, Z., Cheng, S., et al. (2025) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas67066.2025.00068
“Invasive brain-machine interfaces (BMIs) based on high-throughput recording devices have already enabled significant applications, such as speech reconstruction [1-3] and precise motor control [4].”
Nanomaterial-Enabled Sensing for Coupling of Implantable Bcis and Prosthesis
Chen, Z. (2026) · Highlights in Science Engineering and Technology · doi:10.54097/ydt6kj53
“Recently, researchers have achieved an online accuracy rate of approximately 90 characters per minute and 94% by decoding natural handwritten intents, providing a referenceable example for high-bandwidth intent reading [10].”
Meier, L. J. (2026) · Neuroethics · doi:10.1007/s12152-025-09628-y · Cited by 1
“Chisholm, for example, maintains that ‘for every x, if x has the property of being sad, and if x considers the question whether he is sad, then it is certain for x that he is sad’ ([41, 14]).”
InfluentialXu, K., He, L., Chen, H., et al. (2025) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai68704.2025.11345870
“EEG provides high temporal precision at the millisecond scale and is inexpensive and easy to deploy, which makes it well-suited for studying rapid neural dynamics [15].”
A Brain-Computer Interface with Enhanced Power Spectrum for SSVEPs Classification
Zhang, C., Wang, J., Jin, Z., et al. (2025) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1109/icsps66615.2025.11347813
“Currently, BCI systems can achieve functions such as handwriting decoding [3], video games control [4], and can also provide sensory feedback via different methods [5][6].”
Wang, F., Chen, Y., Wang, P., Gong, A., Xu, J., & Fu, Y. (2026) · Scientific Data · doi:10.1038/s41597-026-06708-3 · Cited by 2
“Although substantial progress has been made in invasive handwriting imagery BCI studies 4,11,12 , non-invasive EEG-based decoding of handwriting imagery still suffers from a severe lack of data.”
InfluentialPreserved Embedding of Neural Population Activity for Brain Computer InterfacesPreprint
Jiang, H., Liu, Z., Bu, X., Liu, A., Ji, X., & Yao, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.05.704020
“Brain-computer interfaces (BCIs) restore motor function to paralyzed patients by decoding neural activity from the motor cortex into prosthetic movements [1–5].”
Bidirectional interface between the human brain and neural culture: current state of researchPreprint
Swan, D. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.18648832
CLEP-Based EEG to Text Reconstruction
Wang, L., Qiu, Z., Jiang, Q., Zhang, L., Tang, X. T., & Luo, X. (2026) · IEEE Access · doi:10.1109/access.2026.3665623
“Using this BCI system, research participants paralyzed due to spinal cord injury achieved a typing speed of 90 characters per minute, with an online raw accuracy rate of 94.1% [14].”
InfluentialOptimizing Federated Block-Term Tensor Regression: Strategy Comparisons and Applications
Faes, A., & Peeters, L. M. (2025) · doi:10.1109/fllm67465.2025.11391186
Closed-loop error damping in human BCI using pre-error motor cortex activityPreprint
Gontier, C., Hockeimer, W., Kunigk, N. G., Canario, E., Endsley, L. J., Downey, J. E., Weiss, J. M., Dekleva, B. M., & Collinger, J. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.25.707999
“State-of-the-art methods have allowed BCI study participants to efficiently access a computer [5–8] and even write letters through imagined handwriting [9].”
Cross-subject decoding of human neural data for speech Brain Computer InterfacesPreprint
Boccato, T., Olak, M., & Ferrante, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.27.708564
Control of cortical population activity with patterned microstimulationPreprint
Barzon, G., De, A., Morán, I., Carnahan, C., Mazzucato, L., & Kiani, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.02.709018 · Cited by 1
Lin, Z.-H., & Woo, J.-C. (2026) · Educational Gerontology · doi:10.1080/03601277.2026.2617493
Dual-Manifold Contrastive Learning for Robust and Real-Time EEG Motor Decoding
Hu, C., Liu, Q., Xu, C. F., et al. (2026) · Sensors · doi:10.3390/s26061783
“Moreover, its 100 ms temporal resolution satisfies the timing requirements for closed-loop neurofeedback, allowing for near-instantaneous feedback [45–47].”
The evolution of speech communication devices for anarthria: a review
Jones, C. T., & Hill, E. R. (2026) · Journal of Neurology · doi:10.1007/s00415-026-13734-z
AI-driven innovations in neuroscience: Rewiring precision drug design for neurological disorders
Ali, A. M., Almuqdadi, H. T. A., Hindi, A. A., Murshidy, M. K., Qazi, S., Temur, A. A., Razooqi, N. F., Murshedi, G. H. M. A., & Waheed, M. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44430-2.00021-9
Wang, Z., Xu, G., Yu, B., Xu, K., Zhu, J., Pan, G., Zhang, J., Wang, Y., & Hao, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-70536-7
“…new insights for the development of high-performance handwriting BCls. Incontrasttopreviousstudiesthathavemostlyfocusedonthe2D writing traces 6,23,24,31 , we comprehensively characterized handwriting as multidimensional movement and provided an analysis of the kinematic and kinetic…”
InfluentialChan, P. K. L., & Tao, Z. O. U. (2026) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci69045.2026.11435062
“There is also a necessity of large-area coverage to fully resolve HG activity across distributed brain networks, crucial not only for decoding accuracy, but also for mapping epileptic foci[14] and revealing the coupling relationship among different cortical areas[15].”
Neurolinguistics in Translation and Interpreting Studies
Habig, A.-K., Hansen‐Schirra, S., & Oster, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-95504-1.01625-2
Leinders, 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. (2026) · Scientific Reports · doi:10.1038/s41598-026-44228-7 · Cited by 8
APCformer: an aggregation-perception enhanced convolutional transformer network for MI-EEG decoding
Huang, J., Zou, J., Li, X., Cai, Y., Lin, B., Li, Y., & Xia, X. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1766883
“Their applications are gradually extending into everyday life (Willett et al., 2021; Mansour et al., 2025).”
Geetha, S., Thrisha, M., Gowda, V., & Varsha, K. (2025) · International Conference on Computational Innovations and Sustainable Technologies (ICCIST) · doi:10.1109/iccist67338.2025.11438651 · Cited by 1
“Invasive ECoG systems (Willett et al. [16], Anumanchipalli et al. [20]) achieve 85–95%, while non-invasive EEG approaches (Park et al. [8], CS-eTaR [23]) reach 65–75%.”
Emerging insights in human brain and behavior from intracranial recordings
Tong, A. P. (2026) · Exploration of neuroscience · doi:10.37349/en.2026.1006131
Nan, J., Xu, P., Li, D., Zhu, F., Yang, C., & Zhang, H. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133463
Bilodeau, G., Miao, A., Gagnon-Turcotte, G., Éthier, C., & Gosselin, B. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.25.714179
“The decoder output can be used to drive closed-loop optical stimulation. and stimulation control [4], [16], [17], employ simple or inflexible decoding algorithms [18], [19], or use custom ASICs that lack versatility and are rarely integrated into wireless in vivo experimental setups [20], [21],…”
Yu, Y., Wentong, L. I. U., Ju, S., He, L., Chen, N., Chernov, A. N., Zhang, J., Mao, J., & Liu, G. (2026) · CNS Neuroscience & Therapeutics · doi:10.1002/cns.70851
Towards Brain-Machine Intelligence: Co-Evolution Brain-Computer Interface
Ming, D., Mei, J., & Xu, M.-P. (2025) · Journal of Brain-Computer Interface · doi:10.58195/bci.2025.1.001
Artificial Intelligence-Based Brain-Computer Interfaces: A Key Direction in BCI Development
Li, Z., Yan, L., Cao, Y.-Y., Wang, F., Chen, Y., Li, T., Zhao, L., Xu, J.-P., & Fu, Y. (2025) · Journal of Brain-Computer Interface · doi:10.58195/bci.2025.1.010
Ethical risks and considerations of brain-controlled and neuromodulation technologies
Huang, K., Yang, H., Zhu, S., Chen, Y., Li, T., Zhao, L., Gong, A., Nan, W., Xu, J., & Fu, Y. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-026-10445-z
In search of invariants in electroencephalography signals for brain-computer interfaces
Xu, X. (2023) · theses.fr (ABES) · doi:10.70675/a10e24bbz2b86z4604z87e0zec83c09a8f17
EEG correlates of well-being and wearable EEG
Cannard, C. (2022) · theses.fr (ABES) · doi:10.70675/df2fc223ze70cz4c74zbdeez427a8788428b
Transhumanism and the ethical imperative of preserving and expanding consciousness
Vandenheever, D. (2026) · Journal of Interdisciplinary Ethical Research · doi:10.4102/jier.v2i1.25
A visual Brain-Computer Interface for gaze-free communication : Algorithms & applications
Kerchove, A. V. D. (2024) · theses.fr (ABES) · doi:10.70675/7ee5680eze506z4dbdz8078z01924f3e063a
Patashov, D., Liu, L. H., Tominaga, J., Nakajima, K., Miyanaga, H., Tsunematsu, S., Kato, T., Tanaka, K., & Sakai, H. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04093-x
“It was shown before that a recording from surgically implanted electrode exhibited successful decoding with high accuracy and fine time resolution [6, 7].”
Transformer Based Brain to Text Communication System for Locked-In Syndrome Patients
Thakur, D., Nehru, A., Chauhan, Y., et al. (2026) · International Conference on Innovative Practices in Technology and Management (ICIPTM) · doi:10.1109/iciptm69057.2026.11465389
“In fact, the best studies using the intracortical technique have been able to break the 94% accuracy barrier in handwriting and voice decoding using such highly sophisticated models [3], [4], [8].”
Neural Reconstruction in Disorders of Consciousness: Prospects and Emerging Challenges
Kolisnyk, M., Rankaduwa, S., Gupta, G., Kazazian, K., Debicki, D., & Owen, A. M. (2026) · The Neuroscientist · doi:10.1177/10738584261434939
“For example, Willett et al (2021) asked a quadriplegic patient to imagine writing individual characters from sentences while 2 surgically implanted microelectrode arrays recorded electrical activity from the “hand knob” region of the precentral gyrus.”
InfluentialDTWICA: A Novel Method for Constructing Character Templates in Imaginary Handwriting
Nan, J., Xu, P., Fan, G., Jin, X., Zhai, S., Li, Y., Xia, Y., Meng, Y., Yue, L., & Li, D. (2026) · Information · doi:10.3390/info17040379
Des techniques d’immortalité : l’hypermythe de l’existence numérique
Georges, F. (2026) · e-Phaïstos · doi:10.4000/163vp
Jha, N. K., Liu, C. P., Rogers, A. C., & Lozano, A. (2026) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2026.100366
“4,5 Human BCI’s roots trace to the late 1960s, where researchers first showed that electrical stimulation of the visual cortex in a blind volunteer can elicit light perception (Figure).”
Fedorov, M. V., Repin, D. A., Voshev, D. V., et al. (2026) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2026-6902
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
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