Emotion Recognition Using EEG Signal Based on Support Vector Machine and Highly Reliable Validation Set

Chang Yuan He, Wai Chi Fang*

*Corresponding author for this work

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

Abstract

This work aims at building robust model for human mental state classification using EEG signals. We elaborated a highly reliable data validation set for emotion detection and chose support vector machine (SVM) as the classifier. The results of classification were evaluated by the characteristics observed on the output probability curve. The average accuracy and the maximum accuracy among the subjects of the proposed model achieved 78.28% and 97.50% respectively for the binary-class task.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728132792
DOIs
StatePublished - May 2019
Event6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019 - Yilan, Taiwan
Duration: 20 May 201922 May 2019

Publication series

Name2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019

Conference

Conference6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
CountryTaiwan
CityYilan
Period20/05/1922/05/19

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