An Improved EEG-Based LRCN Emotion Recognition System Using Fuzzy Processing on ECG and PPG Features

Meng Ting Wan, Yi Kai Chen, Wai-Chi Fang*

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this paper, we proposed an improved 3 classes emotion classification system based on a Long-Term Recurrent Convolutional Network (LRCN) model using Electroencephalogram (EEG) signals, reinforced with a fuzzification process on extracted Electrocardiogram (ECG) and Photoplethysmogram (PPG) features. Although a good average accuracy can be achieved at 75%, the accuracy for some specific subjects remained very poor, mostly caused by a low correlation between EEG signal and emotion in a particular subject, or by a lowquality EEG signal recording. The fuzzification process on extra physiological signals was added for EEG-based LRCN to improve the total average accuracy by 8% and correct some low correlated EEG signals and emotions for certain subjects.

原文English
主出版物標題2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665433280
DOIs
出版狀態Published - 2021
事件8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 - Penghu, 台灣
持續時間: 15 9月 202117 9月 2021

出版系列

名字2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021

Conference

Conference8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
國家/地區台灣
城市Penghu
期間15/09/2117/09/21

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