Selecting the optimal EEG electrode positions for a cognitive task using an Artificial Bee Colony with Adaptive Scale Factor optimization algorithm

The present work introduces a proposed Artificial Bee Colony with Adaptive Scale Factor (ABC-ASF) optimization algorithm-based optimal electrode selection strategy from which the acquired EEG signals enlighten the major brain activities involved in a cognitive task. In ABC-ASF, the scale factor for mutation in traditional Artificial Bee Colony is self adapted by learning from the previous experiences. Experimental results obtained from the real framework of estimating optimal electrodes indicate that the proposed algorithm outperforms other state-of-art techniques with respect to computational accuracy and run-time complexity.