Classification of Meditation EEG Signals Using Empirical Wavelet Transform and Atom Search Optimization-Based Feature Selection
OICC Press Journals December 8, 2025 Elham Simakani, Iman Ahanian, Mahdi Eslami et al.
Classifying EEG signals during meditation is difficult because of noise and high data dimensionality. A new framework combines Empirical Wavelet Transform (EWT) for adaptive sub-band decomposition and Atom Search Optimization (ASO) for selecting the most informative features. EEG signals were broken into five standard frequency bands, and eight statistical features per band were extracted. ASO reduced dimensionality while preserving key information. Among several machine learning models tested, Support Vector Machine (SVM) with feature compression achieved the highest classification accuracy of 95%, outperforming baseline and state-of-the-art methods. This approach improves both accuracy and computational speed, making it suitable for brain-computer interfaces and meditation monitoring.