Machine translation of cortical activity to text with an encoder-decoder framework
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 recurre...
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| Published in: | Nature neuroscience Vol. 23; no. 4; pp. 575 - 582 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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United States
Nature Publishing Group
01.04.2020
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| ISSN: | 1097-6256, 1546-1726, 1546-1726 |
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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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| AbstractList | 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.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. 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.Makin and colleagues decode speech from neural signals recorded during a preoperative procedure, using an algorithm inspired by machine translation. For one participant reading from a closed set of 50 sentences, decoding accuracy is nearly perfect. 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. |
| Author | Makin, Joseph G Chang, Edward F Moses, David A |
| Author_xml | – sequence: 1 givenname: Joseph G orcidid: 0000-0002-0053-7006 surname: Makin fullname: Makin, Joseph G email: makin@phy.ucsf.edu, makin@phy.ucsf.edu organization: Department of Neurological Surgery, UCSF, San Francisco, CA, USA. makin@phy.ucsf.edu – sequence: 2 givenname: David A surname: Moses fullname: Moses, David A organization: Department of Neurological Surgery, UCSF, San Francisco, CA, USA – sequence: 3 givenname: Edward F orcidid: 0000-0003-2480-4700 surname: Chang fullname: Chang, Edward F email: edward.chang@ucsf.edu, edward.chang@ucsf.edu organization: Department of Neurological Surgery, UCSF, San Francisco, CA, USA. edward.chang@ucsf.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32231340$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2020. |
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| SubjectTerms | Accuracy Adult Algorithms Brain - physiology Brain-Computer Interfaces Cerebral cortex Coders Decoding Electrocorticography Female Humans Machine translation Middle Aged Neural networks Neural Networks, Computer Recurrent neural networks Representations Speech Speech Perception Transfer learning Translation Words (language) |
| Title | Machine translation of cortical activity to text with an encoder-decoder framework |
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