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The use of electroencephalogram (EEG) as the main input signal inbrain-machine interfaces has been widely proposed due to the non-invasivenature of the EEG. Here we are specifically interested in interfaces thatextract information from the auditory system and more specifically in the taskof classifying heard speech from EEGs. To do so, we propose to limit thepreprocessing of the EEGs and use machine learning approaches to automaticallyextract their meaningful characteristics. More specifically, we use a regulatedrecurrent neural network (RNN) reservoir, which has been shown to outperformclassic machine learning approaches when applied to several differentbio-signals, and we compare it with a deep neural network approach. Moreover,we also investigate the classification performance as a function of the numberof EEG electrodes. A set of 8 subjects were presented randomly with 3 differentauditory stimuli (English vowels a, i and u). We obtained an excellentclassification rate of 83.2% with the RNN when considering all 64 electrodes. Arate of 81.7% was achieved with only 10 electrodes.

Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir