Recognition of human emotions using EEG signals: A review

Assessment of the cognitive functions and state of clinical subjects is an important aspect of e-health care delivery, and in the development of novel human-machine interfaces. A subject can display a range of emotions that significantly influence cognition, and emotion classification through the analysis of physiological signals is a key means of detecting emotion. Electroencephalography (EEG) signals have become a common focus of such development compared to other physiological signals because EEG employs simple and subject-acceptable methods for obtaining data that can be used for emotion analysis. We have therefore reviewed published studies that have used EEG signal data to identify possible interconnections between emotion and brain activity. We then describe theoretical conceptualization of basic emotions, and interpret the prevailing techniques that have been adopted for feature extraction, selection, and classification. Finally, we have compared the outcomes of these recent studies and discussed the likely future directions and main challenges for researchers developing EEG-based emotion analysis methods.

Automated summary

Assessing cognitive functions and clinical states through analyzing physiological signals is crucial in e-health care delivery and human-machine interface development. EEG signals have become a common focus for emotion analysis due to their simplicity and subject acceptance. Researchers have proposed various techniques for feature extraction, selection, and classification to detect emotions. Overcoming challenges and continuously improving methods are essential for the future development of EEG-based emotion analysis.