A Systematic Review of Computational Intelligence Techniques for Channel Selection in P300-Based Brain Computer Interface Speller

Abstract

Electroencephalography (EEG) based P300 speller aid in restoring the communication and control capabilities in patients suffering from motor disabilities. However, the quality and quantity of the data collected from EEG recordings have a substantial influence on the P300 speller's performance. Hence, selecting the optimum number of recording electrodes, i.e., channels for each user, is a significant difficulty for the P300 speller. There are two fundamental objectives of the channel selection process: 1) To extract the most crucial information from the relevant channels, hence reducing the computing complexity of P300/Non-P300 signal processing operation, and 2) To lessen the potential overfitting that could result from using unwanted channels to boost performance. For obtaining the best channel subsets, different channel selection techniques, including manual, filtering, wrapper, and embedded approaches, have been applied by past researchers. This study presents a comprehensive review of recent developments, present status, challenges, and possible solution associated with channel selection strategies in P300 speller.