A5102839842- EEG and Brain-Computer Interfaces
- Emotion and Mood Recognition
- Blind Source Separation Techniques
- Fault Detection and Control Systems
- Industrial Vision Systems and Defect Detection
- Face and Expression Recognition
- Thermography and Photoacoustic Techniques
- Mineral Processing and Grinding
Suzhou Institute of Biomedical Engineering and Technology
2024
University of Science and Technology of China
2024
University of Science and Technology Beijing
2024
Electroencephalogram (EEG) based emotion recognition has become an important topic in humancomputer interaction and affective computing. However, existing advanced methods still have some problems. Firstly, using too many electrodes will decrease the practicality of EEG acquisition device. Secondly, transformer is not good at extracting local features. Finally, differential entropy (DE) unsuitable for features outside 2-44Hz frequency band. To solve these problems, we designed a neural...
Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an effective technology used for information detection by detecting Event-Related Potentials (ERPs). The current RSVP decoding methods can perform well in EEG signals within a single task, but their performance significantly decreases when directly applied to different tasks without calibration data from the new tasks. This limits rapid and efficient deployment of RSVP-BCI systems categories targets various...
Abstract Emotion recognition based on Electroencephalogram (EEG) has been applied in various fields, including human–computer interaction and healthcare. However, for the popular Valence-Arousal-Dominance emotion model, researchers often classify dimensions into high low categories, which cannot reflect subtle changes emotion. Furthermore, there are issues with design of EEG features efficiency transformer. To address these issues, we have designed TPRO-NET, a neural network that takes...