Minimizing EEG Human Interference: A Study of an Adaptive EEG Spatial Feature Extraction With Deep Convolutional Neural Networks

Emotion is one of the main psychological factors that affects human behavior. Using a neural network model trained with electroencephalography (EEG)-based frequency features has been widely used to accurately recognize human emotions. However, utilizing EEG-based spatial information with popular 2-D...

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Veröffentlicht in:	IEEE transactions on cognitive and developmental systems 2024-12, Vol.16 (6), p.1915-1928
Hauptverfasser:	Deng, Haojin, Wang, Shiqi, Yang, Yimin, Zhao, W. G. Will, Zhang, Hui, Wei, Ruizhong, Wu, Q. M. Jonathan, Lu, Bao-Liang
Format:	Artikel
Sprache:	eng
Schlagworte:	Brain modeling Deep learning Electrodes Electroencephalography electroencephalography (EEG) Emotion recognition feature combination Feature extraction Sensor fusion Task analysis
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