  This paper proposes a novel approach that uses deep neural networks for
classifying imagined speech, significantly increasing the classification
accuracy. The proposed approach employs only the EEG channels over specific
areas of the brain for classification, and derives distinct feature vectors
from each of those channels. This gives us more data to train a classifier,
enabling us to use deep learning approaches. Wavelet and temporal domain
features are extracted from each channel. The final class label of each test
trial is obtained by applying a majority voting on the classification results
of the individual channels considered in the trial. This approach is used for
classifying all the 11 prompts in the KaraOne dataset of imagined speech. The
proposed architecture and the approach of treating the data have resulted in an
average classification accuracy of 57.15%, which is an improvement of around
35% over the state-of-the-art results.
