Recognition of grammatical classes of imagined speech words using a convolutional neural network and brain signals

Denise Alonso-Vázquez, Tonatiuh Hernández-Del-Toro, Omar Mendoza-Montoya, Ricardo Caraza, Hector R Martinez, Carlos Alberto Reyes-Garcia, Javier M. Antelis

Keywords: Speech decoding, convolutional neural network, imagined speech, electroencephalography

TL;DR: In this paper, using the EEGNet convolutional neural network, we evaluated three data window sizes to classify between two grammatical groups from EEG signals obtained during the imagined speech task of Spanish words.

Abstract: In this paper, we analyze in time domain the signals acquired with 32 electroencephalography (EEG) channels from 10 healthy participants obtained during the imagined speech task of words in Spanish. We performed a statistical test to determine the location in space and time of the differences produced by imagining words from two grammatical classes: decision adverbs and nouns. Based on the statistical test results and using the EEGNet convolutional neural network, we evaluated three different data window sizes for the classification of the two grammatical groups. In the larger window W1 (700ms), we obtained an accuracy of 60.1%, while in the smaller window W3 (200ms), the accuracy obtained was 69.5%. This work is a first approach for the decoding of imagined speech words that are intended to be implemented in a brain-machine interface focused on patients with amyotrophic lateral sclerosis.

Submission Type: Archival (to be published in the Journal of LatinX in AI (LXAI) Research)

Submission Number: 8