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2018 | OriginalPaper | Chapter

An Extended Common Spatial Pattern Framework for EEG-Based Emotion Classification

Authors : Jingxia Chen, Dongmei Jiang, Yanning Zhang

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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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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Metadata
Title
An Extended Common Spatial Pattern Framework for EEG-Based Emotion Classification
Authors
Jingxia Chen
Dongmei Jiang
Yanning Zhang
Copyright Year
2018
Book
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-030-00562-7

Electronic ISBN: 978-3-030-00563-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00563-4_27

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