  In this paper classification of mental task-root Brain-Computer Interfaces
(BCI) is being investigated, as those are a dominant area of investigations in
BCI and are of utmost interest as these systems can be augmented life of people
having severe disabilities. The BCI model's performance is primarily dependent
on the size of the feature vector, which is obtained through multiple channels.
In the case of mental task classification, the availability of training samples
to features are minimal. Very often, feature selection is used to increase the
ratio for the mental task classification by getting rid of irrelevant and
superfluous features. This paper proposes an approach to select relevant and
non-redundant spectral features for the mental task classification. This can be
done by using four very known multivariate feature selection methods viz,
Bhattacharya's Distance, Ratio of Scatter Matrices, Linear Regression and
Minimum Redundancy & Maximum Relevance. This work also deals with a comparative
analysis of multivariate and univariate feature selection for mental task
classification. After applying the above-stated method, the findings
demonstrate substantial improvements in the performance of the learning model
for mental task classification. Moreover, the efficacy of the proposed approach
is endorsed by carrying out a robust ranking algorithm and Friedman's
statistical test for finding the best combinations and comparing different
combinations of power spectral density and feature selection methods.
