Classification of Neuroticism using Psychophysiological Signals During Speaking Task based on Two Different Baseline Measurements

Neuroticism or also known as emotional stability has been interpreted as the capability of a person to remain stable and balanced to the surrounding world People with high neuroticism or known as neurotic persons have low emotional stability where they feel stress easily, have low self-esteem, and tend to have negative emotions such as fear, worry, insecurity and bitterness towards the surrounding world They are also more susceptible to anxiety and depression than others On the other hand, people with low neuroticism have high emotional stability where they Abstract: Biosignals from psychophysiological changes can be measured as electroencephalography (EEG), heart rate, skin conductance, and respiration rate, to name a few. They have been used in many research areas including human personality. Neuroticis m, one of the five major traits underlie personality, reflects stable tendency towards experiencing negative emotions. An understanding of how neuroticism influences responses to psychological distress may shed a light upon individual d ifferences in emotion self-regulat ion. To study the causal relat ionship between neuroticism and psychophysiological signals, a selection of appropriate baseline signals as a reference signal is essential to co mpare to current experimental signals of interest. Thus, we present classification of neuroticism using psychophysiological signals obtained during a speaking task based on two different baseline measurements (eyes closed and eyes open). Eight healthy male part icipants consisting of four neurotic and four emotionally stable subjects were recru ited based on Eysenck Personality Inventory (EPI) and Big Five Inventory (BFI) scoring system. Four features including mean EEG beta power, heart rate, skin conductance, and respiration rate were used for the classification using a Support Vector Machine (SVM). The results showed higher classification accuracy achieved with eyes open as the baseline (62.5%) as co mpared to eyes closed as the baseline (37.5%), during speaking task. Th is indicate the importance of selecting appropriate baseline in analysis involving EEG and physiological signals.