Speech Recognition using EEG signals recorded using dry electrodes

TL;DR

This paper explores speech recognition through EEG signals recorded with dry electrodes, achieving promising accuracy and demonstrating the feasibility of this approach for limited vocabulary recognition.

Contribution

It introduces a deep learning-based method for speech recognition using dry electrode EEG signals on a small vocabulary, showing promising results.

Findings

Test accuracy of 79.07% on two vowels

Feasibility of dry EEG electrodes for speech recognition

Potential for expanding EEG-based speech interfaces

Abstract

In this paper, we demonstrate speech recognition using electroencephalography (EEG) signals obtained using dry electrodes on a limited English vocabulary consisting of three vowels and one word using a deep learning model. We demonstrate a test accuracy of 79.07 percent on a subset vocabulary consisting of two English vowels. Our results demonstrate the feasibility of using EEG signals recorded using dry electrodes for performing the task of speech recognition.