The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

• Main Authors: Wirawan, I Made Agus, Wardoyo, Retantyo, Lelono, Danang
• Format: Other NonPeerReviewed
• Language: English
• Published: International Journal of Electrical and Computer Engineering 2022
• Subjects: Electronic and Instrumentation System
• Online Access: https://repository.ugm.ac.id/284299/1/The-challenges-of-emotion-recognition-methods-based-on-electroencephalogram-signals-A-literature-reviewInternational-Journal-of-Electrical-and-Computer-Engineering.pdf; https://repository.ugm.ac.id/284299/; https://ijece.iaescore.com/index.php/IJECE/article/view/25953
• Institution: Universitas Gadjah Mada

Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.