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





electroencephalography, P300 speller, channel selection


Electroencephalography (EEG)-based P300 speller aids 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 research provides an in-depth examination of recent advancements, status, challenges, and potential solutions related to channel selection strategies in P300 speller systems. Each channel selection technique is thoroughly explored, including detailed comparisons between them. The notable advantages and drawbacks of each method are emphasized along with the discussion on the future direction and scope of work in the field of channel selection in P300 speller. The review underscores that channel selection methods enable the use of a reduced number of channels without compromising classification performance. By eliminating noisy or irrelevant channels, these approaches contribute to enhanced system performance.


Received: 21 July 2023 | Revised: 3 January 2024 | Accepted: 29 February 2024


Conflicts of Interest

Narendra D. Londhe is an Editorial Board Member for Artificial Intelligence and Applications, and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.


Data Availability Statement

Data sharing is not applicable to this article as no new data were

created or analyzed in this study.


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How to Cite

Bhandari, V. ., Londhe, N. D., & Kshirsagar, G. B. (2024). A Systematic Review of Computational Intelligence Techniques for Channel Selection in P300-Based Brain Computer Interface Speller. Artificial Intelligence and Applications, 2(3), 169–178. https://doi.org/10.47852/bonviewAIA42021390