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EEG channel selection using metaheuristic algorithms in alcoholism detection using optimal wavelet transform.

Digambar V Puri¹, Pramod Kachare², Sandeep Sangle²

¹Department of Information Technology, Vidhyalankar Institute of Technology, Wadala, Mumbai, Maharashtra, 400 037, India.

Scientific Reports

|May 18, 2026

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This summary is machine-generated.

This study introduces an effective electroencephalography (EEG) channel selection method using metaheuristic algorithms for alcoholism detection. The approach significantly improves accuracy and reduces computational time compared to traditional methods.

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Alcoholism detection lacks standardized objective methods, relying on time-consuming and error-prone subjective assessments.
Electroencephalography (EEG) offers a promising objective measure by analyzing brain electrical activity for alcoholism detection.
Existing EEG analysis methods for alcoholism can be computationally intensive and may not fully optimize channel utilization.

Purpose of the Study:

To develop and evaluate an effective EEG channel selection technique for accurate and efficient alcoholism detection.
To investigate the utility of metaheuristic algorithms (MHAs) in identifying optimal EEG channels and features for distinguishing alcoholic from non-alcoholic individuals.
To compare the performance of optimized EEG channel selection against using all channels in classification models.

Keywords:

Alcoholism Discrete wavelet transform Electroencephalogram Filter banks Halfband filter Support vector machine

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Main Methods:

EEG signals were decomposed into subbands using a novel optimal wavelet filter bank (OWFB).
Four features (mean, Higuchi's fractal dimension, log entropy, Rényi's entropy) were extracted from each subband.
Combinations of four MHAs and six classification models were employed to find optimal subband features and channels using a public 64-channel EEG dataset.

Main Results:

The Sparrow Search Algorithm combined with the k-Nearest Neighbors (KNN) classifier achieved the highest accuracy (95.90%) and F1-score (96.80%) with optimal channel selection.
Performance with optimal channel selection was comparable to using all EEG channels (96.30% accuracy, 96.83% F1-score).
The KNN classifier consistently demonstrated superior performance across different channel selection strategies.

Conclusions:

Optimal EEG channel selection using MHAs effectively reduces channel redundancy while maintaining high accuracy in alcoholism detection.
This approach enhances machine learning model performance and significantly reduces computational time compared to existing methods.
The study provides a robust and efficient method for objective alcoholism detection using EEG data.