  Brain-computer interface systems and the recording of brain activity has
garnered significant attention across a diverse spectrum of applications. EEG
signals have emerged as a modality for recording neural electrical activity.
Among the methodologies designed for feature extraction from EEG data, the
method of RCSP has proven to be an approach, particularly in the context of MI
tasks. RCSP exhibits efficacy in the discrimination and classification of EEG
signals. In optimizing the performance of this method, our research extends to
a comparative analysis with conventional CSP techniques, as well as optimized
methodologies designed for similar applications. Notably, we employ the
meta-heuristic multi-objective Strength Pareto Evolutionary Algorithm II
(SPEA-II) as a pivotal component of our research paradigm. This is a
state-of-the-art approach in the selection of an subset of channels from a
multichannel EEG signal with MI tasks. Our main objective is to formulate an
optimum channel selection strategy aimed at identifying the most pertinent
subset of channels from the multi-dimensional electroencephalogram (EEG)
signals. One of the primary objectives inherent to channel selection in the EEG
signal analysis pertains to the reduction of the channel count, an approach
that enhances user comfort when utilizing gel-based EEG electrodes.
Additionally, within this research, we took benefit of ensemble learning models
as a component of our decision-making. This technique serves to mitigate the
challenges associated with overfitting, especially when confronted with an
extensive array of potentially redundant EEG channels and data noise. Our
findings not only affirm the performance of RCSP in MI-based BCI systems, but
also underscore the significance of channel selection strategies and ensemble
learning techniques in optimizing the performance of EEG signal classification.
