Fusing multichannel neurophysiological signals to recognize human emotion states
becomes increasingly attractive. The conventional methods ignore the complementarity …

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph
(EEG) processing techniques to recognize emotion states is proposed in this paper. Three …

Emotion recognition from EEG signals is a major field of research in cognitive computing.
The major challenges involved in the task are extracting meaningful features from the …

Recently, much attention has been attracted to automatic emotion recognition based on
multi-channel electroencephalogram (EEG) signals, with the rapid development of machine …

The electroencephalogram (EEG) signal is a medium to realize a brain–computer interface
(BCI) system due to its zero clinical risk and portable acquisition devices. As deep learning …

Electroencephalogram (EEG)-based emotion recognition is receiving significant attention in
research on brain-computer interfaces (BCI) and health care. To recognize cross-subject …

Featured Application The proposed method in this study can be used in EEG emotion
recognition and achieve better results. Abstract In recent years, deep learning has been …

Emotion recognition (ER) from Electroencephalogram (EEG) signals is a challenging task
due to the non-linearity and non-stationarity nature of EEG signals. Existing feature …

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data
have been drawing attention thanks to their capability in countering the effect of deceptive …

Recently, emotional electroencephalography (EEG) has been of great importance in brain–
computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG …