Optimized EEG Channel Selection Using Power-Based Ranking and PSD Feature Modelling for EEG Signal Analysis

DOI

10.2991/978-94-6239-713-2_11How to use a DOI?

Keywords

Electroencephalogram; power-based ranking; Power spectral density; Welch’s method; channel selection

Abstract

An electroencephalogram (EEG) is a non-invasive method used to measure brain activity and is used in various applications, such as medical, security, marketing, gaming, and brain-computer interface (BCI). EEG signals are high-dimensional and redundant in nature, which significantly complicates the channel selection process in terms of computational and analytical robustness. It is one of the critical steps in the EEG signal analysis, as the selected channels should preserve the task-relevant information without any loss. The optimal channel selection technique has been proposed using the power-based ranking and the Power Spectral Density (PSD) method. The power-based ranking method selects 9 channels from the 64 channels and extracts 9 features from the selected channels using Welch's method. Coupling features like Magnitude Squared Coherence (MAC) and Phase Amplitude Coupling (PAC) are fetched from the selected channels. Channel optimality is compared and evaluated using the spectral entropy metric, and the system achieved an average spectral entropy of 0.7234 ± 0.0216. The result shows that the proposed technique achieves high performance, reduces the system complexity, and enhances the system interoperability by reducing the number of EEG channels.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - C. Kaviyazhiny
AU  - P. Shanthi Bala
AU  - S. Ajeeth
AU  - R. Priyadharshini
PY  - 2026
DA  - 2026/06/25
TI  - Optimized EEG Channel Selection Using Power-Based Ranking and PSD Feature Modelling for EEG Signal Analysis
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 145
EP  - 158
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_11
DO  - 10.2991/978-94-6239-713-2_11
ID  - Kaviyazhiny2026
ER  -