Signal Processing Algorithms Based on Evolutionary Optimization Techniques in the BCI: A Review

Brain–computer interfaces (BCIs) collect, analyze and transform brain signals into commands that are linked to desirable tasks involved by target equipment. The feature extraction process is used to deploy an alternate solution interpretation of signal obtained, which made way a collection of BCI actions more effectively. Pre-processing phase involving re-referencing of electrodes, deterioration, normalization, size reduction and removal of artifacts, etc. is often used before feature extraction. The classification of features was examined in this paper, and evolutionary technique is applied to BCI. The implications of the paper can be helpful for academicians, researchers and scientists in this domain to quickly understand the previous work in this field. These algorithms can be used in various applications such as the classification of motor imagery tasks, filter banks, etc.