  This work explores the possibility of decoding Imagined Speech (IS) signals
which can be used to create a new design of Human-Computer Interface (HCI).
Since the underlying process generating EEG signals is unknown, various feature
extraction methods, along with different neural network (NN) models, are used
to approximate data distribution and classify IS signals. Based on the
experimental results, feed-forward NN model with ensemble and covariance matrix
transformed features showed the highest performance in comparison to other
existing methods. For comparison, three publicly available datasets were used.
We report a mean classification accuracy of 80% between rest and imagined
state, 96% and 80% for decoding long and short words on two datasets. These
results show that it is possible to differentiate brain signals (generated
during rest state) from the IS brain signals. Based on the experimental
results, we suggest that the word length and complexity can be used to decode
IS signals with high accuracy, and a BCI system can be designed with IS signals
for computer interaction. These ideas, and results give direction for the
development of a commercial level IS based BCI system, which can be used for
human-computer interaction in daily life.

Signal Processing;Human-Computer Interaction;Machine Learning;Sound;Audio and Speech Processing