Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary char...

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Vydané v:	IEEE transactions on neural systems and rehabilitation engineering Ročník 32; s. 1637 - 1646
Hlavní autori:	Pang, Miaoqi, Wang, Hongtao, Huang, Jiayang, Vong, Chi-Man, Zeng, Zhiqiang, Chen, Chuangquan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adult Algorithms Brain modeling Brain-Computer Interfaces cross-session Data collection Data mining Data models EEG EEG-based emotion recognition Electroencephalography Electroencephalography - methods Emotion recognition Emotional factors Emotions Emotions - physiology Feature extraction Female Human-computer interface Humans Invariants Machine Learning Male Neural Networks, Computer Representations Robustness self-supervised learning Task analysis transformer
ISSN:	1534-4320, 1558-0210, 1558-0210
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