title: EEG sensor based classification for assessing psychological stress.
abstract: Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preliminary result indicates that EEG sensor based classification could be an efficient technique for evaluation of the psychological state of individuals. Thus, the system can be used for personal health monitoring in order to improve users health.
authors.affiliation: School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
authors.forename: Shahina
authors.initials: S
authors.lastname: Begum
authors.forename: Shaibal
authors.initials: S
authors.lastname: Barua
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013315:Stress, Psychological / Q000175:diagnosis* / Q000503:physiopathology*