DGAFF: Deep genetic algorithm fitness Formation for EEG Bio-Signal channel selection

Abstract

Brain-computer interface systems aim to facilitate human–computer interactions by directly translating brain signals for computers. Recently, using many electrodes has led to better performance in these systems. However, increasing the number of recorded electrodes causes additional time, hardware, and computational costs besides undesired complications of the recording process. Channel selection decreases data dimension and eliminates irrelevant channels while reducing the noise effects. Furthermore, the technique lowers the time and computational costs in the test phase. We present a channel selection method, which combines a sequential search method with a genetic algorithm called Deep Genetic Algorithm Fitness Formation (DGAFF). The proposed method accelerates the convergence of the genetic algorithm and increases the system’s performance. The system evaluation is based on a lightweight deep neural network that automates the whole model training process. Our method, compared to other channel selection methods, outperforms the tradeoff between the classification accuracy and the number of selected channels.