Imagined Speech Classification Using Six Phonetically Distributed Words

Abstract

Imagined speech can be used to send commands without any muscle movement or emitting audio. The current status of research is in the early stage, and there is a shortage of open-access datasets for imagined speech analysis. We have proposed an openly accessible electroencephalograph (EEG) dataset for six imagined words in this work. We have selected six phonetically distributed, monosyllabic, and emotionally neutral words from W-22 CID word lists. The phonetic distribution of words consisted of the different places of consonants’ articulation and different positions of tongue advancement for vowel pronunciation. The selected words were “could,” “yard,” “give,” “him,” “there,” and “toe.” The experiment was performed over 15 subjects who performed the overt and imagined speech task for the displayed word. Each word was presented 50 times in random order. EEG signals were recorded during the experiment using a 64-channel EEG acquisition system with a sampling rate of 2,048 Hz. A preliminary analysis of the recorded data is presented by performing the classification of EEGs corresponding to the imagined words. The achieved accuracy is above the chance level for all subjects, which suggests that the recorded EEGs contain distinctive information about the imagined words.