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Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis.

Nilotpal Das¹, Monisha Chakraborty²

¹Biomedical Instrumentation Laboratory, School of Bioscience & Engineering, Jadavpur University, Kolkata, India.

Computers in Biology and Medicine

|July 7, 2025

Summary

This study reveals that combining various neural dynamics features with the k-nearest neighbors (KNN) classifier accurately decodes mental states from electroencephalography (EEG) signals during music listening.

Keywords:

Classification Electroencephalography Feature selection Machine learning Music perception

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Published on: November 9, 2011

Area of Science:

Neuroscience
Cognitive Science
Signal Processing

Background:

Music perception is crucial for cognitive and emotional processing.
Understanding neural dynamics of music perception aids in neuroscientific investigation.

Purpose of the Study:

Examine neural dynamics in music perception.
Identify optimal methods for mental state classification using electroencephalography (EEG) signals.

Main Methods:

Utilized Indian classical music (ICM) to evoke distinct cognitive states.
Extracted diverse EEG features (statistical, covariance, wavelet, fractal, entropy).
Employed machine learning classifiers (SVM, KNN, perceptron, tree-based) optimized via Bayesian Optimization.

Main Results:

Classification performance varied significantly with feature and classifier choice.
Covariance features with KNN yielded high accuracy.
Combined features and KNN achieved up to 98.65% accuracy, indicating shared and distinct neural patterns.

Conclusions:

Music perception involves complex neural coordination across brain regions.
Multimodal feature combinations enhance classification performance for mental states.