  In this work, we explore the possibility of decoding Imagined Speech brain
waves using machine learning techniques. We propose a covariance matrix of
Electroencephalogram channels as input features, projection to tangent space of
covariance matrices for obtaining vectors from covariance matrices, principal
component analysis for dimension reduction of vectors, an artificial
feed-forward neural network as a classification model and bootstrap aggregation
for creating an ensemble of neural network models. After the classification,
two different Finite State Machines are designed that create an interface for
controlling a computer system using an Imagined Speech-based BCI system. The
proposed approach is able to decode the Imagined Speech signal with a maximum
mean classification accuracy of 85% on binary classification task of one long
word and a short word. We also show that our proposed approach is able to
differentiate between imagined speech brain signals and rest state brain
signals with maximum mean classification accuracy of 94%. We compared our
proposed method with other approaches for decoding imagined speech and show
that our approach performs equivalent to the state of the art approach on
decoding long vs. short words and outperforms it significantly on the other two
tasks of decoding three short words and three vowels with an average margin of
11% and 9%, respectively. We also obtain an information transfer rate of
21-bits-per-minute when using an IS based system to operate a computer. These
results show that the proposed approach is able to decode a wide variety of
imagined speech signals without any human-designed features.
