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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Bibliographic Details
Published in:	Nature neuroscience Vol. 23; no. 4; pp. 575 - 582
Main Authors:	Makin, Joseph G, Moses, David A, Chang, Edward F
Format:	Journal Article
Language:	English
Published:	United States Nature Publishing Group 01.04.2020
Subjects:	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)
ISSN:	1097-6256, 1546-1726, 1546-1726
Online Access:	Get full text
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