Abstract

Emotion recognition is currently a topic that researchers widely have discussed. This is due to the significant influence of emotions on everyday life. It is not only affecting human health, but also playing an essential role in decision making. Electroencephalogram (EEG) is one of the physiological signals that can be used to measure and recognize emotions based on data from human brain activity. In this study, EEG-based happy and sad emotions classification were performed using time-domain features in the range of EEG bands low alpha(8-10Hz), high alpha(10-13Hz), low beta(13-20Hz), and high beta(20-30Hz) from four different channels namely Fp1, Fp2, F7 and F8 in 10/20 EEG system. The EEG data was obtained from 25 adult subjects. Statistical features such as Mean Absolute Value (MAV), maximum, and variance are used to distinguish between happy and sad emotions. Four classification models were tested and analyzed, namely: logistic regression, SVM, Long-Short Term Memory (LSTM), and bidirectional LSTM. The experiment result showed that the MAV feature contributed to the highest accuracy (95%) using bidirectional LSTM compared to other features. By applying the MAV feature on four different classifiers mentioned above, the highest accuracy was obtained in beta high EEG band.

Original languageEnglish
Title of host publicationProceeding - ICERA 2021
Subtitle of host publication2021 3rd International Conference on Electronics Representation and Algorithm
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages89-94
Number of pages6
ISBN (Electronic)9781665434003
DOIs
Publication statusPublished - 29 Jul 2021
Event3rd International Conference on Electronics Representation and Algorithm, ICERA 2021 - Virtual, Yogyakarta, Indonesia
Duration: 29 Jul 2021 → …

Publication series

NameProceeding - ICERA 2021: 2021 3rd International Conference on Electronics Representation and Algorithm

Conference

Conference3rd International Conference on Electronics Representation and Algorithm, ICERA 2021
Country/TerritoryIndonesia
CityVirtual, Yogyakarta
Period29/07/21 → …

Keywords

  • EEG signal
  • LSTM
  • bidirectional LSTM
  • emotion classification
  • happy and sad emotions

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