Journal
IEEE SENSORS JOURNAL
Volume 20, Issue 12, Pages 6532-6541Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.2976159
Keywords
Electroencephalography; Feature extraction; Physiology; Public speaking; Wearable sensors; Task analysis; Personality traits; wearable sensors; physiological signals; feature extraction; classification
Ask authors/readers for more resources
Personality traits are fundamental parameters to characterize an individual's behavior. Recently physiological signals are used to recognize personality traits in response to emotional videos, but gives good accuracies only for few traits. In this study, personality traits are statistically analyzed and recognized using physiological signals (electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmogram (PPG)) in response to public speaking. The proposed framework consists of data acquisition, pre-processing, feature extraction, feature selection, and classification stages. The subject is labeled into two classes of each personality trait based on the mean value of big five personality trait questionnaire score. The physiological signals of twenty-eight participants are recorded while performing a public speaking task in front of a real audience. These signals are statistically analyzed to examine the behavior of different personality traits. Time domain features from GSR and PPG data and frequency domain features from EEG data are extracted. A wrapper based method for feature selection is applied to select an optimum set of features from each modality, which are then fused for personality trait recognition. An average F1-score of 0.86, 0.96, 0.64, 0.75, and 0.86 is achieved by the proposed framework for openness to experience, extroversion, neuroticism, conscientiousness, and agreeableness traits respectively. It is observed that the personality trait recognition using physiological signals in response to public speaking task outperforms the existing methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available