Autors: Pereira, V., Tavares, F., Mihaylova, P., Mladenov, V. M., Georgieva, P.
Title: Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions
Keywords: Factor Analysis, Neural Descriptors, electroencephalogram, b

Abstract: Amajor challenge in decoding human emotions fromelectroencephalogram(EEG) data is finding representations that are invariant to inter- and intrasubject differences. Most of the previous studies are focused in building an individual discrimination model for every subject (subject dependentmodel). Building subject-independent models is a harder problem due to the high data variability between different subjects and different experiments with the same subject.This paper explores, for the first time, the Factor Analysis as an efficient technique to extract temporal and spatial EEG features suitable to build brain-computer interface for decoding human emotions across various subjects. Our findings show that early waves (temporal window of 200–400ms after the stimulus onset) carrymore information about the valence of the emotion. Also, spatial location of features, with a stronger impact on the emotional valence, occurs in the parietal and occipital regions of the brain. All discriminationm



    Complexity, vol. 2018, pp. 1-8, 2018, United Kingdom, Hindawi, DOI 10.1155/2018/6740846

    Цитирания (Citation/s):
    1. Zhou, R., Ou, Y., Tang, W., Wang, Q., Yu, B. An emergency evacuation behavior simulation method combines personality traits and emotion contagion, IEEE Access 8,9057621, pp. 66693-66706 - 2020 - в издания, индексирани в Scopus или Web of Science
    2. Isik, U., Guven, A. Classification of emotion from physiological signals via artificial intelligence techniques, TIPTEKNO 2019 - Tip Teknolojileri Kongresi 8895087 - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Bian, W., Wang, C., Ye, Z., Yan, L. Emotional Text Analysis Based on Ensemble Learning of Three Different Classification Algorithms, Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2019 2,8924413, pp. 938-941 - 2019 - в издания, индексирани в Scopus или Web of Science
    4. IŞIK, Ümran, and Ayşegül GÜVEN. "Yapay Zeka Teknikleri ile Fizyolojik Sinyallerde Duygu Durum Sınıflandırması Classification of Emotion from Physiological Signals via Artificial Intelligence Techniques." - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    5. 32. Gu, F. and Xiao, Y., 2021. Social Network Structure as a Moderator of the Relationship between Psychological Capital and Job Satisfaction: Evidence from China. Complexity, 2021. - 2021 - в издания, индексирани в Scopus или Web of Science
    6. Kandaleft, D., Murayama, K., Roesch, E. and Sakaki, M., 2022. Resting-state functional connectivity does not predict individual differences in the effects of emotion on memory. Scientific reports, vol. 12, issue (1), pp.1-14., ISSN 20452322, DOI 10.1038/s41598-022-18543-8 (Web of Science, Scopus) IF 5.516, SJR 1.005 - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science