@inproceedings{c552b434484a4b208559fbd8d10f16cc,
title = "An Extended Common Spatial Pattern Framework for EEG-Based Emotion Classification",
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.",
keywords = "CSP, DSA, EEG, Emotion classification",
author = "Jingxia Chen and Dongmei Jiang and Yanning Zhang",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 ; Conference date: 07-07-2018 Through 08-07-2018",
year = "2018",
doi = "10.1007/978-3-030-00563-4_27",
language = "英语",
isbn = "9783030005627",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "282--292",
editor = "Amir Hussain and Bin Luo and Jiangbin Zheng and Xinbo Zhao and Cheng-Lin Liu and Jinchang Ren and Huimin Zhao",
booktitle = "Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings",
}