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Classification of EEG Signals Utilizing DWT for Feature Extraction and Evolutionary Algorithms for Feature Selection

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posted on 2023-12-02, 20:45 authored by Mayur AkewarMayur Akewar

This paper introduces an EEG signal classification approach, leveraging machine learning algorithms. The methodology involves the extraction of features from EEG signal datasets through discrete wavelet transform (DWT). Optimal feature selection is then accomplished using evolutionary algorithms, specifically Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). To identify the most effective classification method, various machine learning algorithms, including Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forest, are systematically compared. This comprehensive evaluation aims to enhance the accuracy and efficiency of EEG signal classification for improved diagnosis and understanding of neurological conditions.

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Email Address of Submitting Author

mayurakewar87@gmail.com

ORCID of Submitting Author

0009-0001-0343-6761

Submitting Author's Institution

Shri Ramdeobaba College of Engineering and Management

Submitting Author's Country

  • India

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