Emotion recognition based on multi-channel electroencephalograph (EEG) signals is
becoming increasingly attractive. However, the conventional methods ignore the spatial …

Video affective content analysis has been an active research area in recent decades, since
emotion is an important component in the classification and retrieval of videos. Video …

Deep learning-based emotion recognition using EEG has received increasing attention in
recent years. The existing studies on emotion recognition show great variability in their …

Existing correlations between features extracted from Electroencephalography (EEG)
signals and emotional aspects have motivated the development of a diversity of EEG-based …

Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key
research area in the field of affective computing. Traditional methods extract EEG features …

Human emotions are variant with time, non-stationary, complex in nature, and are invoked
as a result of human reactions during our daily lives. Continuously detecting human …

In recent years, artificial intelligence (AI) technology has promoted the development of
electroencephalogram (EEG) emotion recognition. However, existing methods often …

Emotion recognition based on the multi-channel electroencephalograph (EEG) is becoming
increasingly attractive. However, the lack of large datasets and privacy concerns lead to …

We present a novel framework for decoding individuals? emotional state and personality
traits based on physiological responses to affective movie clips. During watching 36 video …

In this study, we propose the discrete cosine transform coefficients as a new and effective set
of features for recognizing patterns of brain activity in MEG recording. We claim that …