  The use of electroencephalogram (EEG) as the main input signal in
brain-machine interfaces has been widely proposed due to the non-invasive
nature of the EEG. Here we are specifically interested in interfaces that
extract information from the auditory system and more specifically in the task
of classifying heard speech from EEGs. To do so, we propose to limit the
preprocessing of the EEGs and use machine learning approaches to automatically
extract their meaningful characteristics. More specifically, we use a regulated
recurrent neural network (RNN) reservoir, which has been shown to outperform
classic machine learning approaches when applied to several different
bio-signals, and we compare it with a deep neural network approach. Moreover,
we also investigate the classification performance as a function of the number
of EEG electrodes. A set of 8 subjects were presented randomly with 3 different
auditory stimuli (English vowels a, i and u). We obtained an excellent
classification rate of 83.2% with the RNN when considering all 64 electrodes. A
rate of 81.7% was achieved with only 10 electrodes.
