  The recent advances in the field of deep learning have not been fully
utilised for decoding imagined speech primarily because of the unavailability
of sufficient training samples to train a deep network. In this paper, we
present a novel architecture that employs deep neural network (DNN) for
classifying the words "in" and "cooperate" from the corresponding EEG signals
in the ASU imagined speech dataset. Nine EEG channels, which best capture the
underlying cortical activity, are chosen using common spatial pattern (CSP) and
are treated as independent data vectors. Discrete wavelet transform (DWT) is
used for feature extraction. To the best of our knowledge, so far DNN has not
been employed as a classifier in decoding imagined speech. Treating the
selected EEG channels corresponding to each imagined word as independent data
vectors helps in providing sufficient number of samples to train a DNN. For
each test trial, the final class label is obtained by applying a majority
voting on the classification results of the individual channels considered in
the trial. We have achieved accuracies comparable to the state-of-the-art
results. The results can be further improved by using a higher-density EEG
acquisition system in conjunction with other deep learning techniques such as
long short-term memory.
