Abstract
Emotional electroencephalography (EEG) signals are a primary means of recording emotional brain activity.Currently, the most effective methods for analyzing emotional EEG signals involve feature engineering and neuralnetworks. However, neural networks possess a strong ability for automatic feature extraction. Is it possible to discardfeature engineering and directly employ neural networks for end-to-end recognition? Based on the characteristics of EEGsignals, this paper proposes an end-to-end feature extraction and classification method for a dynamic self-attention network(DySAT). The study reveals significant differences in brain activity patterns associated with different emotions acrossvarious experimenters and time periods. The results of this experiment can provide insights into the reasons behind thesedifferences.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
