Prediction of subjective ratings of emotional pictures by EEG features

• Published in: Journal of neural engineering Vol. 14; no. 1; p. 16009
• Main Authors: McFarland, Dennis J, Parvaz, Muhammad A, Sarnacki, William A, Goldstein, Rita Z, Wolpaw, Jonathan R
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.02.2017
• Subjects: Adult; Aged; Algorithms; Arousal - physiology; Cerebral Cortex - physiology; EEG; Electroencephalography - methods; emotion; Emotions - physiology; Feasibility Studies; Humans; Middle Aged; Pattern Recognition, Automated - methods; Photic Stimulation - methods; rehabilitation; Reproducibility of Results; Sensitivity and Specificity; Visual Perception - physiology
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/14/1/016009

Objective. Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. Approach. To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. Main results. Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. Significance. The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.