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EEG-induced Fear-type Emotion Classification Through Wavelet Packet Decomposition, Wavelet Entropy, and SVM

Year 2022, Volume: 9 Issue: 4, 241 - 251, 31.12.2022
https://doi.org/10.17350/HJSE19030000277

Abstract

Among the most significant characteristics of human beings is their ability to feel emotions. In recent years, human-machine interface (HM) research has centered on ways to empower the classification of emotions. Mainly, human-computer interaction (HCI) research concentrates on methods that enable computers to reveal the emotional states of humans. In this research, an emotion detection system based on visual IAPPS pictures through EMOTIV EPOC EEG signals was proposed. We employed EEG signals acquired from channels (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) for individuals in a visual induced setting (IAPS fear and neutral aroused pictures). The wavelet packet transform (WPT) combined with the wavelet entropy algorithm was applied to the EEG signals. The entropy values were extracted for every two classes. Finally, these feature matrices were fed into the SVM (Support Vector Machine) type classifier to generate the classification model. Also, we evaluated the proposed algorithm as area under the ROC (Receiver Operating Characteristic) curve, or simply AUC (Area under the curve) was utilized as an alternative single-number measure. Overall classification accuracy was obtained at 91.0%. For classification, the AUC value given for SVM was 0.97. The calculations confirmed that the proposed approaches are successful for the detection of the emotion of fear stimuli via EMOTIV EPOC EEG signals and that the accuracy of the classification is acceptable.

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References

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Year 2022, Volume: 9 Issue: 4, 241 - 251, 31.12.2022
https://doi.org/10.17350/HJSE19030000277

Abstract

Project Number

-

References

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  • [26] V. Padhmashree and A. Bhattacharyya, “Human emotion recognition based on time–frequency analysis of multivariate EEG signal,” Knowledge-Based Syst., vol. 238, 2022, doi: 10.1016/j.knosys.2021.107867.
  • [27] H. Liu, J. Zhang, Q. Liu, and J. Cao, “Minimum spanning tree based graph neural network for emotion classification using EEG,” Neural Networks, vol. 145, 2022, doi: 10.1016/j.neunet.2021.10.023.
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Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Çağlar Uyulan 0000-0002-6423-6720

Ahmet Ergun Gümüş 0000-0002-2044-5504

Zozan Güleken 0000-0002-4136-4447

Project Number -
Publication Date December 31, 2022
Submission Date March 3, 2022
Published in Issue Year 2022 Volume: 9 Issue: 4

Cite

Vancouver Uyulan Ç, Gümüş AE, Güleken Z. EEG-induced Fear-type Emotion Classification Through Wavelet Packet Decomposition, Wavelet Entropy, and SVM. Hittite J Sci Eng. 2022;9(4):241-5.

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