DECS: A Deep Learning Approach for EEG Channel Selection in Emotion Classification

Muhammad Saad Hussain; Areebah Mubarak; Amna Arshad

Authors

Muhammad Saad Hussain Software Engineering Department (Army Public College of Management Sciences, APCOMS).
Areebah Mubarak Department of Cyberspace Science and Technology (Beijing Institute of Technology, Haidan District, Beijing).
Amna Arshad Computer Science and Technology Department (Beijing Institute of Technology, Haidan District, Beijing).

Keywords:

Bag of Deep Features, Continuous Wavelet Transform, Differential Entropy-based channel selection, Support Vector Machine

Abstract

The non-stationary nature of Electroencephalogram (EEG) signals often leads to high computational complexity in emotion recognition systems. To address this, we propose a novel framework that integrates optimal channel selection with efficient feature extraction. Our method begins by converting preprocessed EEG signals into two-dimensional spectrograms using a Continuous Wavelet Transform (CWT). These spectrograms are then processed by a GoogLeNet model for deep feature extraction. A key contribution is the Differential Entropy-based Channel Selection (DECS) technique, which identifies and retains the most informative channels. To manage dimensionality, the extracted features are encoded using the Bag-of-Deep-Features (BoDF) method, which employs k-means clustering to create a visual vocabulary and represents features as histograms. Finally, these histogram features are classified using a Support Vector Machine (SVM). Evaluated on the SJTU SEED and DEAP datasets, the proposed model achieves state-of-the-art classification accuracies of 95.1% and 81.1%, respectively, demonstrating its effectiveness and efficiency.

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