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Emotion recognition is an integral part of affective computing. An affective brain-computer-interface (BCI) can benefit the user in a number of applications. In most existing studies, EEG (electroencephalograph)-based emotion recognition is explored in a classificatory manner. In this manner, human emotions are discretized by a set of emotion labels. However, human emotions are more of a continuous...
In human–computer interaction (HCI), electroencephalogram (EEG) signals can be added as an additional input to computer. An integration of real-time EEG-based human emotion recognition algorithms in human–computer interfaces can make the users experience more complete, more engaging, less emotionally stressful or more stressful depending on the target of the applications. Currently, the most accurate...
An integration of real-time EEG-based human emotion recognition algorithms in brain-computer interfaces can make the user's experience more complete, more engaging, less emotionally stressful or more stressful depending on the target of the application. Valence component of emotion, level of pleasantness, is one of the most important criteria of online assessment of social processes from brain signals...
Stability of algorithms is very important for electroencephalogram (EEG) based applications. Stable features should exhibit consistency among repeated measurements of the same subject. Previously, power features were reported to be one of the most stable EEG features in medical application. In this paper, stability of features in emotion recognition algorithms is studied. Our hypothesis is that the...
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