Research on emotion recognition using sparse EEG channels and cross-subject modeling based on CNN-KAN-F2CA model

• Published in: PloS one Vol. 20; no. 5; p. e0322583
• Main Authors: Xiong, Fan, Fan, Mengzhao, Yang, Xu, Wang, Chenxiao, Zhou, Jinli
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 27.05.2025
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Biology and Life Sciences; Brain research; Channels; Classification; Color imagery; Computer and Information Sciences; Datasets; Deep learning; EEG; Electroencephalography; Emotion recognition; Emotions; Engineering and Technology; Fast Fourier transformations; Feature extraction; Fourier transforms; Frequency dependence; Frequency domain analysis; Machine learning; Medicine and Health Sciences; Methods; Neural networks; Physical Sciences; Physiology; Power spectral density; Research and Analysis Methods; Social Sciences; Spatial data; Support vector machines; Wavelet transforms; China

Emotion recognition plays a significant role in artificial intelligence and human-computer interaction. Electroencephalography (EEG) signals, due to their ability to directly reflect brain activity, have become an essential tool in emotion recognition research. However, the low dimensionality of spa...