@inproceedings{c009a1f7fbe94ca9b367ad7558f39bc7,
title = "Emotion Recognition Using EEG Signal Based on Support Vector Machine and Highly Reliable Validation Set",
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.",
author = "He, {Chang Yuan} and Fang, {Wai Chi}",
year = "2019",
month = may,
doi = "10.1109/ICCE-TW46550.2019.8991761",
language = "English",
series = "2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019",
address = "United States",
note = "6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019 ; Conference date: 20-05-2019 Through 22-05-2019",
}