A feature-optimized EEG framework using multiscale entropy and metaheuristic optimization for cognitive analysis.
- 1School of Electronics Engineering, Vellore Institute of Technology Chennai, Chennai, Tamil Nadu, India.
Abstract
Background
Cognitive activity plays an important role by influencing working-memory, problem-solving, attention, response and decision making. EEG serves as a non-invasive approach to analyze neural activity associated with cognitive processing. The current study aims to identify the more contributing features to analyze the cognitive activity between sports and non-sports groups.Methods
The present study included 100 participants, consisting of 80 males and 20 females aged between 19 and 23 years. EEG signals were acquired and preprocessed using filtering methods to remove unwanted noise and artifacts. The four-entropy metrics dispersion entropy, permutation entropy, sample entropy, and fuzzy entropy were applied to the coarse-graining method across multiple temporal scales to analyze the complexity of EEG signals. To identify the most informative feature subsets and reduce redundancy, filter-based methods such as Mutual Information, Pearson Correlation, Minimum Redundancy Maximum Relevance and wrapper-based methods such as Genetic Algorithm and Particle Swarm Optimization were applied. Finally for differentiating the cognitive activity, five machine learning classifiers that include Support Vector Machine, Artificial Neural Network, K-nearest neighbor, Decision Tree and Random Forest were applied. Statistical methods such as T-test and Fleiss kappa were used to analyse the performance efficiency.Results
Feature extraction phase yielded a total of 160 multiscale entropy features. The entire features when fed into both filter-based and wrapper-based methods, Genetic Algorithm based optimization yielded a minimal contributing feature subset with high nested cross-validated accuracy of 93.0 ± 2.7% with Support Vector Machine. This contributing feature subset was used as input for five different machine learning classifiers for differentiating the cognitive activity between both groups. The five classifiers demonstrated high cross-validated agreement on held-out subjects, with a mean Fleiss' kappa of 0.874 ± 0.051 (95% CI: 0.811-0.937), depicting a good inter-classifier agreement for classification of samples into sports and non-sports individuals.Discussion
The study demonstrates that multiscale entropy features combined with metaheuristic-based feature optimization can effectively analyze the cognitive activity between sports and non-sports individuals. The proposed EEG framework provides a structured approach for real-time cognitive activity analysis using compact and informative entropy-based feature subsets.
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BibTeX
@article{keerthika2026featureoptimized,
title = {{A feature-optimized EEG framework using multiscale entropy and metaheuristic optimization for cognitive analysis.}},
author = {Keerthika N and Kiruthika V},
journal = {Frontiers in artificial intelligence},
year = {2026},
month = oct,
doi = {10.3389/frai.2026.1888234},
url = {https://europepmc.org/article/MED/42898558},
note = {CC BY},
}
RIS
TY - JOUR
TI - A feature-optimized EEG framework using multiscale entropy and metaheuristic optimization for cognitive analysis.
AU - Keerthika N
AU - Kiruthika V
PY - 2026
DA - 2026/10/05
JO - Frontiers in artificial intelligence
DO - 10.3389/frai.2026.1888234
UR - https://europepmc.org/article/MED/42898558
AB - Background Cognitive activity plays an important role by influencing working-memory, problem-solving, attention, response and decision making. EEG serves as a non-invasive approach to analyze neural activity associated with cognitive processing. The current study aims to identify the more contributing features to analyze the cognitive activity between sports and non-sports groups.Methods The present study included 100 participants, consisting of 80 males and 20 females aged between 19 and 23 years. EEG signals were acquired and preprocessed using filtering methods to remove unwanted noise and artifacts. The four-entropy metrics dispersion entropy, permutation entropy, sample entropy, and fuzzy entropy were applied to the coarse-graining method across multiple temporal scales to analyze the complexity of EEG signals. To identify the most informative feature subsets and reduce redundancy, filter-based methods such as Mutual Information, Pearson Correlation, Minimum Redundancy Maximum Relevance and wrapper-based methods such as Genetic Algorithm and Particle Swarm Optimization were applied. Finally for differentiating the cognitive activity, five machine learning classifiers that include Support Vector Machine, Artificial Neural Network, K-nearest neighbor, Decision Tree and Random Forest were applied. Statistical methods such as T-test and Fleiss kappa were used to analyse the performance efficiency.Results Feature extraction phase yielded a total of 160 multiscale entropy features. The entire features when fed into both filter-based and wrapper-based methods, Genetic Algorithm based optimization yielded a minimal contributing feature subset with high nested cross-validated accuracy of 93.0 ± 2.7% with Support Vector Machine. This contributing feature subset was used as input for five different machine learning classifiers for differentiating the cognitive activity between both groups. The five classifiers demonstrated high cross-validated agreement on held-out subjects, with a mean Fleiss' kappa of 0.874 ± 0.051 (95% CI: 0.811-0.937), depicting a good inter-classifier agreement for classification of samples into sports and non-sports individuals.Discussion The study demonstrates that multiscale entropy features combined with metaheuristic-based feature optimization can effectively analyze the cognitive activity between sports and non-sports individuals. The proposed EEG framework provides a structured approach for real-time cognitive activity analysis using compact and informative entropy-based feature subsets.
SN - 2624-8212
ER -