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Decoding Natural Behavior from Neuroethological Embedding

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Machine translation of cortical activity to text with an encoder-decoder framework.

Joseph G Makin^1,2, David A Moses^3,4, Edward F Chang^5,6

¹Center for Integrative Neuroscience, UCSF, San Francisco, CA, USA. makin@phy.ucsf.edu.

Nature Neuroscience

|April 2, 2020

Summary

This summary is machine-generated.

Researchers achieved high-accuracy speech decoding from brain signals using a recurrent neural network. This advance brings brain-computer interface technology closer to natural speech rates.

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Area of Science:

Neuroscience
Biomedical Engineering
Artificial Intelligence

Background:

Speech decoding from brain signals has progressed significantly over the past decade.
Current methods still lag behind natural speech in terms of accuracy and speed.

Purpose of the Study:

To develop a high-accuracy, natural-speech-rate decoding method for electrocorticography (ECoG) signals.
To leverage machine translation principles for improved brain-computer interface (BCI) performance.

Main Methods:

A recurrent neural network (RNN) was trained to encode neural activity into an abstract representation.
The RNN then decoded this representation into English sentences word-by-word.
Data included ECoG signals from ~250 electrodes and spoken sentence repetitions from participants.

Main Results:

Achieved average word error rates as low as 3% on held-out data.
Demonstrated decoding at natural speech rates.
Showcased improvement in decoding with limited data through transfer learning across participants.

Conclusions:

The developed RNN-based approach significantly enhances speech decoding accuracy and speed from ECoG.
Transfer learning offers a promising strategy to improve BCI performance with limited individual participant data.
This work represents a substantial step towards seamless communication for individuals with speech impairments.