  In Brain-Computer Interfacing (BCI), due to inter-subject non-stationarities
of electroencephalogram (EEG), classifiers are trained and tested using EEG
from the same subject. When physical disabilities bottleneck the natural
modality of performing a task, acquisition of ample training data is difficult
which practically obstructs classifier training. Previous works have tackled
this problem by generalizing the feature space amongst multiple subjects
including the test subject. This work aims at knowledge transfer to classify
EEG of the target subject using a classifier trained with the EEG of another
unit source subject. A many-objective optimization framework is proposed where
optimal weights are obtained for projecting features in another dimension such
that single source-trained target EEG classification performance is maximized
with the modified features. To validate the approach, motor imagery tasks from
the BCI Competition III Dataset IVa are classified using power spectral density
based features and linear support vector machine. Several performance metrics,
improvement in accuracy, sensitivity to the dimension of the projected space,
assess the efficacy of the proposed approach. Addressing single-source training
promotes independent living of differently-abled individuals by reducing
assistance from others. The proposed approach eliminates the requirement of EEG
from multiple source subjects and is applicable to any existing feature
extractors and classifiers. Source code is available at
http://worksupplements.droppages.com/tlbci.html.
