@inproceedings{39bf7ff038ae440c955718393e42f3ea,
title = "Emotion Recognition Based on Electroencephalogram Using a Multiple Instance Learning Framework",
abstract = "Electroencephalogram (EEG)-based emotion recognition has been widely researched in the field of affective computing. Nevertheless, EEG signals which reflect brain activity are always unstable, it is inappropriate for traditional analysis methods to treat each sliding time window of signals as independent sample during classification. In this study, we employ a multi-instance learning (MIL) framework for EEG-based emotion recognition and regard sliding time windows from the same EEG signal as a whole by learning two MIL models based on Citation-kNN and mi-SVM algorithms. Experiment results show that our methods can achieve higher classification accuracy of 74.21\% and 77.50\% on two affective dimensions (valence and arousal) respectively when comparing with traditional single-instance classification algorithms. We believe that MIL framework can improve the generalization performance of EEG-based emotion recognition further, and provide new inspiration for affective computing.",
keywords = "Affective computing, Electroencephalogram, Emotion recognition, Multi-instance Learning",
author = "Xiaowei Zhang and Yue Wang and Shengjie Zhao and Jinyong Liu and Jing Pan and Jian Shen and Tingzhen Ding",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 14th International Conference on Intelligent Computing, ICIC 2018 ; Conference date: 15-08-2018 Through 18-08-2018",
year = "2018",
doi = "10.1007/978-3-319-95933-7\_66",
language = "English",
isbn = "9783319959320",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "570--578",
editor = "De-Shuang Huang and Kang-Hyun Jo and Xiao-Long Zhang",
booktitle = "Intelligent Computing - 14th International Conference, ICIC 2018, Proceedings",
address = "Germany",
}