Machine translation of cortical activity to text with an encoder–decoder framework
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Abstract
A decade after speech was first decoded from human brain signals, accuracy and speed remain far below that of natural speech. Here we show how to decode the electrocorticogram with high accuracy and at natural-speech rates. Taking a cue from recent advances in machine translation, we train a recurrent neural network to encode each sentence-length sequence of neural activity into an abstract representation, and then to decode this representation, word by word, into an English sentence. For each participant, data consist of several spoken repeats of a set of 30-50 sentences, along with the contemporaneous signals from ~250 electrodes distributed over peri-Sylvian cortices. Average word error rates across a held-out repeat set are as low as 3%. Finally, we show how decoding with limited data can be improved with transfer learning, by training certain layers of the network under multiple participants' data.
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In previous multiparticipant studies in able speakers, participants had near-identical speech capacity.
“By contrast, in previous multiparticipant studies in able speakers, participants had near-identical speech capacity106,108,136–138.”
From The speech neuroprosthesis (2024) · cites this paper as [108]
The Methods (Task; page not provided) say that participants read sentences aloud, supporting the description of this as a multiparticipant study of able speakers. Neither that section nor the Methods participant description (page not provided) establishes that their speech capacities were near-identical. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTransfer learning between individuals or between different types of speech within the same individual may expedite decoder training.
“Transfer learning, either between individuals108 or different types of speech within the same individual120, may also be used to expedite decoder training.”
From The speech neuroprosthesis (2024) · cites this paper as [108]
The Results section (‘Transfer learning’; page not provided) reports that adding other sentences spoken by the same participant improved decoding of the target sentence set. This supports the cited point that related speech data may help decoder training, but the paper tests different sentence sets, not clearly different types of speech. The between-individual claim is attached to a different citation. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMakin et al. applied an RNN encoder-decoder framework to encode temporal patterns in neural activity during sentence production into an abstract representation that was then decoded word-by-word into phrases.
“To build on this result and move beyond direct classification of words or sentences, Makin et al.108 applied a recurrent neural network (RNN) encoder-decoder framework to encode temporal patterns in neural activity during sentence production into an abstract representation that was then decoded word-by-word into phrases (Fig. 1; Makin et al. 2020).”
From The speech neuroprosthesis (2024) · cites this paper as [108]
Speech-decoding BCIs show promise for rapid communication, but their accuracy and vocabulary size still need improvement for general-purpose use.
“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.”
From High-performance brain-to-text communication via handwriting (2021) · cites this paper as [17]
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- Authors
- Makin, J. G., Moses, D. A., & Chang, E. F.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Makin, Joseph G.
- Chang, Edward F.
- Venue
- Nature Neuroscience
- Published
- 2020-03-30
- Volume
- 23
- Issue
- 4
- Pages
- 575-582
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC10560395
- OpenAlex
- W3013691153
- Cited by
- 321
- References
- 45
- Citations by year
- 2026: 21 · 2025: 47 · 2024: 55 · 2023: 47 · 2022: 71 · 2021: 58 · 2020: 21 · 2019: 1
- FWCI
- 19.35
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (U01 NS098971)
- Facebook Sponsored Academic Research Agreement
- Facebook (Sponsored Academic Research Agreement)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- neural speech decoding, electrocorticography, machine translation, encoder-decoder architecture, recurrent neural networks, cortical activity, transfer learning, word error rate, brain-computer interface
- MeSH terms
- Electrocorticography, Adult, Brain, Female, Humans, Middle Aged, Speech, Speech Perception, Neural Networks, Computer, Brain-Computer Interfaces
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