Autors: Velchev, Y. S., Radeva S., Sokolov S., Radev D.
Title: Automated estimation of human emotion from EEG using statistical features and SVM
Keywords: EEG, Human Emotions, SVM

Abstract: This paper presents an approach for automated estimation of human emotions from electroencephalogram data. The used features are principally the Hjorth parameters calculated for theta, alpha, beta and gamma bands taken from certain channels. The classification stage is support vector machine. Since the human emotions are modelled as combinations from physiological elements such as arousal, valence, dominance, liking, etc., these quantities are the classifier's outputs. The best achieved correct classification performance is about 80%.

References

    Issue

    2016 Digital Media Industry and Academic Forum, DMIAF 2016 - Proceedings, pp. 40-42, 2016, Greece, IEEE, DOI 10.1109/DMIAF.2016.7574899

    Copyright IEEE

    Цитирания (Citation/s):
    1. Zhang, R., Yan, B., Tong, L., Shu, J., Song, X., Zeng, Y. Identity Authentication Using Portable Electroencephalography Signals in Resting States (2019) IEEE Access, 7, art. no. 8887439, pp. 160671-160682, DOI: 10.1109/ACCESS.2019.2950366 - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Alarcão, S. M., & Fonseca, M. J. (2019). Emotions recognition using EEG signals: A survey. IEEE Transactions on Affective Computing, 10(3), 374-393. doi:10.1109/TAFFC.2017.2714671 - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Hazarika, J., "Analyzing the resting-state EEG of action video game players using wavelet transform", 2019 3rd International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2019, pp. 362-367, doi:10.1109/RDCAPE47089.2019.8979079 - 2019 - в издания, индексирани в Scopus или Web of Science
    4. Chen, DW (Chen, Dongwei); Yang, WQ (Yang, Weiqi); Miao, R (Miao, Rui); Huang, L (Huang, Lan); Zhang, L (Zhang, Liu); Deng, CJ (Deng, Chunjian); Han, N (Han, Na), " Novel joint algorithm based on EEG in complex scenarios", COMPUTER ASSISTED SURGERY Volume: 24 Special Issue: SI Pages: 117-125 DOI: 10.1080/24699322.2019.1649078 - 2019 - в издания, индексирани в Scopus или Web of Science
    5. Acharya, D., Varshney, N., Vedant, A., Saxena, Y., Tomar, P., Goel, S., Bhardwaj, A., "An enhanced fitness function to recognize unbalanced human emotions data", Expert Systems with Applications, 166, 114011, 2021, DOI: 10.1016/j.eswa.2020.114011 - 2021 - в издания, индексирани в Scopus или Web of Science
    6. Wijasena, H.Z., Ferdiana, R., Wibirama, S., "A Survey of Emotion Recognition using Physiological Signal in Wearable Devices", AIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems, art. no. 9466092, 2021, DOI: 10.1109/AIMS52415.2021.9466092 - 2021 - в издания, индексирани в Scopus или Web of Science
    7. Joshi, V.M., Ghongade, R.B., "EEG based emotion detection using fourth order spectral moment and deep learning", (2021) Biomedical Signal Processing and Control, 68, art. no. 102755, DOI: 10.1016/j.bspc.2021.102755 - 2021 - в издания, индексирани в Scopus или Web of Science
    8. Islam, M.R., Islam, M.M., Rahman, M.M., Mondal, C., Singha, S.K., Ahmad, M., Awal, A., Islam, M.S., Moni, M.A., "EEG Channel Correlation Based Model for Emotion Recognition", (2021) Computers in Biology and Medicine, 136, art. no. 104757, DOI: 10.1016/j.compbiomed.2021.104757 - 2021 - в издания, индексирани в Scopus или Web of Science
    9. Ghosh, S.M., Bandyopadhyay, S., Mitra, D., "Nonlinear classification of emotion from EEG signal based on maximized mutual information", (2021) Expert Systems with Applications, 185, art. no. 115605, DOI: 10.1016/j.eswa.2021.115605 - 2021 - в издания, индексирани в Scopus или Web of Science
    10. Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Prabal Datta Barua, Murugappan, M., Chakole, Y., Acharya, U.R., "Automated emotion recognition: Current trends and future perspectives:, (2022) Computer Methods and Programs in Biomedicine, 215, art. no. 106646, DOI: 10.1016/j.cmpb.2022.106646 - 2022 - в издания, индексирани в Scopus или Web of Science
    11. Meng M., Zhang Y., Ma Y., Gao Y., Kong W., "EEG-based emotion recognition with cascaded convolutional recurrent neural networks", (2023) Pattern Analysis and Applications, 26 (2), pp. 783 - 795, DOI: 10.1007/s10044-023-01136-0 - 2023 - в издания, индексирани в Scopus или Web of Science
    12. Zhang X., Huang D., Li H., Zhang Y., Xia Y., Liu J., "Self-training maximum classifier discrepancy for EEG emotion recognition", (2023) CAAI Transactions on Intelligence Technology, 8 (4), pp. 1480 - 1491, DOI: 10.1049/cit2.12174 - 2023 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science