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Online EEG Classification of Covert Speech for Brain-Computer Interfacing.

Alborz Rezazadeh Sereshkeh¹, Robert Trott², Aurélien Bricout³

¹1,* Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M4G1R8, Canada.

International Journal of Neural Systems

|August 24, 2017

Summary

This study introduces intuitive brain-computer interfaces (BCIs) using covert speech, bypassing nonintuitive tasks. Electroencephalography (EEG) allowed participants to communicate "yes" or "no" via mental speech, achieving significant accuracy.

Keywords:

Brain–computer interface EEG covert speech, support vector machine, autoregressive model wavelet transform

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Published on: May 19, 2016

Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Current brain-computer interfaces (BCIs) often require nonintuitive tasks like motor imagery.
There is a need for more intuitive and natural communication methods for BCI users.

Purpose of the Study:

To develop and evaluate two novel online BCIs based solely on covert speech using electroencephalography (EEG).
To assess the feasibility of using mental repetition of words (
yes
no
and rest) for BCI control.

Main Methods:

Developed two EEG-based BCIs: one differentiating
no
from rest, and another differentiating
yes
from
no
.
Utilized a support vector machine with spectral and time-frequency features for classification.
Twelve participants completed offline training and online testing sessions.

Main Results:

Achieved an average accuracy of [Formula: see text] for classifying
no
versus rest, with 10/12 participants exceeding chance level.
Attained an average accuracy of [Formula: see text] for classifying
yes
versus
no
, with 8/12 participants exceeding chance level.
Observed task-specific changes in EEG beta and gamma power in language-related brain areas.

Conclusions:

Demonstrated the first online EEG classification of covert speech for BCI applications.
Covert speech is a promising activation task for developing more intuitive BCIs.
Further research into covert speech BCIs could significantly improve communication for users.