Open access database of EEG signals recorded during imagined speech

Germán A. Pressel Coretto; Iván E. Gareis; H. Leonardo Rufiner

doi:10.1117/12.2255697

26 January 2017 Open access database of EEG signals recorded during imagined speech

Germán A. Pressel Coretto, Iván E. Gareis, H. Leonardo Rufiner

Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 1016002 (2017) https://doi.org/10.1117/12.2255697
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina

Abstract

Brain-Computer Interfaces (BCI) that could decode thoughts into commands would improve the quality of life of patients who have lost control over voluntary muscles. Imagined speech consists in imagining the pronunciation of words, without moving or emitting sounds. In this study, we introduce a new open access database of electroencephalogram (EEG) signals recorded while 15 subjects imagined the pronunciation of two groups of Spanish words. The first one contained the vowels /a/, /e /, /i/, /o/, /u/; and the second one corresponds to the commands up, down, left, right, backward and forward. Each subject repeated each word 50 times in a random order, meanwhile EEG signals were recorded using a six channel acquisition system and sampled at 1024 Hz. For comparison, some blocks were recorded using the pronounced speech condition, in which audio and EEG signals were acquired simultaneously. The EEG signals were filtered for artifact’s removal between 2 Hz and 40 Hz using a finite impulse response (FIR) pass-band filter. As a preliminary analysis of the EEG data, an offline classification method is presented. Accuracy rate is above chance level for almost all subjects, suggesting that EEG signals possess discriminative information about the imagined word.

Citation Download Citation

Germán A. Pressel Coretto, Iván E. Gareis, and H. Leonardo Rufiner "Open access database of EEG signals recorded during imagined speech", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 1016002 (26 January 2017); https://doi.org/10.1117/12.2255697

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