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*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433280
DOIs
StatePublished - 2021
Event8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 - Penghu, Taiwan
Duration: 15 Sep 202117 Sep 2021

Publication series

Name2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021

Conference

Conference8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Country/TerritoryTaiwan
CityPenghu
Period15/09/2117/09/21

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