Brain–computer interface (BCI) technology has emerged as a transformative communication paradigm by enabling direct interaction between the human brain and external computing systems. Electroencephalography (EEG)-based BCIs, particularly P300 speller systems, have demonstrated considerable potential for assistive communication, neurorehabilitation, and intelligent human–computer interaction. However, the high dimensionality of EEG recordings, redundancy among channels, and susceptibility to noise significantly reduce classification accuracy while increasing computational complexity. Consequently, intelligent channel selection has become a critical research area for improving the efficiency, reliability, and scalability of EEG-based BCI systems. This review synthesizes recent developments in computational intelligence approaches for channel selection, including filter, wrapper, embedded, hybrid, and deep learning-based techniques. The article critically examines theoretical foundations, algorithmic characteristics, performance trade-offs, and practical implications of these approaches within modern BCI environments. Furthermore, an integrated intelligent computational framework is proposed to explain how advanced computational intelligence can optimize channel selection while balancing classification accuracy, computational cost, and real-time processing requirements. Comparative analysis of existing studies demonstrates that hybrid computational intelligence strategies consistently outperform conventional statistical approaches by effectively capturing nonlinear EEG relationships and adaptive channel importance. The review also identifies current research challenges involving subject variability, model interpretability, computational scalability, and deployment in real-world clinical environments. Overall, Intelligent Channel Selection Using Advanced Computational Intelligence Methods provides a comprehensive research perspective that supports future development of adaptive, explainable, and computationally efficient EEG signal processing systems.

Brain-Computer Interface, EEG, Channel Selection, Computational Intelligence

Alotaiby, T., El-Samie, F. E.A., Alshebeili, S. A., & Ahmad, I. (2015). A review of channel selection algorithms for EEG signal processing. EURASIP Journal on Advances in Signal Processing, 2015(1), 66.

Baig, M. Z., Aslam, N., & Shum, H. P. H. (2020). Filtering techniques for channel selection in motor imagery EEG applications: A survey. Artificial Intelligence Review, 53(2), 1207–1232.

El Dabbagh, H., & Fakhr, W. (2011). Multiple classification algorithms for the BCI P300 speller diagram using ensemble of SVMs. In IEEE GCC Conference and Exhibition, 393–396.

Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), 510–523.

Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008). Toward enhanced P300 speller performance. Journal of Neuroscience Methods, 167(1), 15–21.

Kshirsagar, G. B., & Londhe, N. D. (2022). DS-P3SNet: An efficient classification approach for Devanagari script-based P300 speller using compact channelwise convolution and knowledge distillation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(12), 7431–7443.

Nijboer, F., Sellers, E. W., Mellinger, J., Jordan, M. A., Matuz, T., Furdea, A., :::, & Kübler, A. (2008). A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clinical Neurophysiology, 119(8), 1909–1916.

Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004). BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering, 51(6), 1034–1043.

Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791.

Zahra, A., Vahid, A., & Sadeghi, M. T. (2013). Comparison of performance of different feature extraction methods in detection of P300. Biocybernetics and Biomedical Engineering, 33(1), 3–20.

K. Ramamurthy, R. K. Konduru and N. Amanmadov, "Evo Graph Coder: An Evolutionary Graph-Reasoning Framework for Self-Adaptive Software Engineering," in IEEE Access, vol. 14, pp. 63063-63076, 2026, doi: 10.1109/ACCESS.2026.3686019.

Geo Philip, Paulson, Integrated Intelligent Building Energy Management: A Multi-Layer Framework for Renewable Energy, HVAC Optimization, and Smart Electrical Network Coordination. Available at SSRN: https://ssrn.com/abstract=6993209 or http://dx.doi.org/10.2139/ssrn.6993209

M. R. Marri, S. B. Kurada, S. Gupta, S. Dey, S. Kumar and R. Chauhan, "Graph-Based Deep Learning Model for Identifying Cyber Threats in Cloud Platforms," 2025 International Conference on Computational Intelligence, Security, and Artificial Intelligence (IntelliSecAI), Al-Khobar, Saudi Arabia, 2025, pp. 1-7, doi: 10.1109/IntelliSecAI66368.2025.11472831.