A growing number of affective computing researches recently developed a computer system
that can recognize an emotional state of the human user to establish affective human …

Recently, deep learning methodologies have become popular to analyse physiological
signals in multiple modalities via hierarchical architectures for human emotion recognition …

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a
user, which is considered an important factor in human-machine-interaction. Many methods …

Abstract Background and Objective Using deep-learning methodologies to analyze
multimodal physiological signals becomes increasingly attractive for recognizing human …

Using machine-learning methodologies to analyze EEG signals becomes increasingly
attractive for recognizing human emotions because of the objectivity of physiological data …

EEG-based emotion recognition enables investigation of human brain activity, which is
recognized as an important factor in brain-computer interface. In recent years, several …

One of the challenges in virtual environments is the difficulty users have in interacting with
these increasingly complex systems. Ultimately, endowing machines with the ability to …

Information theory allows us to investigate information processing in neural systems in terms
of information transfer, storage and modification. Especially the measure of information …

Several methods for collecting psychophysiological data from humans have been
developed, including galvanic skin response (GSR), electromyography (EMG) …

While considerable research has been conducted, the field of neurodesign and human-
computer interaction (N-HCI) has yet to be methodically delineated in light of contemporary …