Abstract
A major challenge for emotion classification using electroencephalography (EEG) is how to effectively extract more discriminative feature and reduce the day-to-day variability in raw EEG data. This study proposed a novel spatial filtering algorithm called Ext-CSP which combined common spatial patterns (CSP) and the regularization term into a unified optimization framework based on Kullback-Leibler (KL) divergence. The experiment was carried out on a five-day Music Emotion EEG dataset of 12 subjects. Four classifiers were applied to make emotion classification. The experiment results demonstrated our unified Ext-CSP algorithm could effectively increase the robustness and generalizability of the extracted EEG features and gain 14% better performance than traditional PCA algorithm, and 1.7% better performance than the stepwise DSA-CSP iteration algorithm on EEG-based emotion classification.
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Chen, J., Jiang, D., Zhang, Y. (2018). An Extended Common Spatial Pattern Framework for EEG-Based Emotion Classification. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_27
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