  Training Brain Computer Interface (BCI) systems to understand the intention
of a subject through Electroencephalogram (EEG) data currently requires
multiple training sessions with a subject in order to develop the necessary
expertise to distinguish signals for different tasks. Conventionally the task
of training the subject is done by introducing a training and calibration stage
during which some feedback is presented to the subject. This training session
can take several hours which is not appropriate for on-line EEG-based BCI
systems. An alternative approach is to use previous recording sessions of the
same person or some other subjects that performed the same tasks (subject
transfer) for training the classifiers. The main aim of this study is to
generate a methodology that allows the use of data from other subjects while
reducing the dimensions of the data. The study investigates several
possibilities for reducing the necessary training and calibration period in
subjects and the classifiers and addresses the impact of i) evolutionary
subject transfer and ii) adapting previously trained methods (retraining) using
other subjects data. Our results suggest reduction to 40% of target subject
data is sufficient for training the classifier. Our results also indicate the
superiority of the approaches that incorporated evolutionary subject transfer
and highlights the feasibility of adapting a system trained on other subjects.
