comparison of 3 different machine learning methods to classify the emotional states using...

Abstracts of the 15th World Congress of Psychophysiology of theInternational Organization of Psychophysiology (IOP)

Comparison of 3 different machine learning methods to classifythe emotional states using physiological responses

Heui Kyung Yanga, Eun-Hye Jangb, Ji-Eun Parka, Ji-Hye Noha,Hyo-Eun Kima, Jin-Hun Sohna

aDepartment of Psychology, Brain Research Institute,Chungnam National University, Republic of KoreabThe Robot/Cognition System Research Department, IT ConvergenceTechnology Research Laboratory, Electronics and TelecommunicationsResearch Institute, Republic of Korea

Objective: The current method used to study emotion recognitionon human–computer interaction is to recognize human emotionsusing physiological signals. This study used autonomic nervoussystem responses as physiological signals, which were coded andanalyzed to recognize human emotional states. Autonomic nervoussystem responses caused by emotion provoking protocols weremeasured and 7 different emotions were classified using machinelearning by physiological signal analysis.

Methods: Six male and six female undergraduate studentsparticipated. Seven different emotion provoking stimuli were pre-sented to the participants and physiological signal responses, i.e.,GSR, ECG, PPG, and SKT were measured. The stimuli were audio-visual film clips that were tested and their appropriateness andeffectiveness were presented for four minutes. Physiological re-sponses that reflect autonomic nervous system activity weremeasured for one minute before emotional state the resting periodand for four minutes during emotional state. This experiment wasdone in seven different emotions and one session a week for fivesessions. MP150 Biopac system Inc. (USA) was used to measureautonomic nervous system responses and AcqKnowledge (version3.8.1) was used to analyze physiological signals. The obtainedphysiological signals were measured for 30 seconds each during therest period and the emotional state and then analyzed, resulting in 26parameters of physiological signals. 7 different emotions wereclassified into 3 classifiers, using Neural Network, Decision Tree,and Discriminant Analysis.

Results: Mean EDA level, number of response SCR and meanamplitude of response from EDA, mean SKT level and maximum SKTfrom SKT, mean volume from PPG, time-domain parameters andfrequency-domain parameters from ECG that all reflect emotionswere obtained. The analysis on obtained 7 different emotions resultedin the classification rate of 63.5% on Neural Network, 20.3% onDecision Tree and 49.3% on Discriminant Analysis.

Discussion: Based on the classification of emotions analysisusing Neural Network, Decision Tree and Discriminant Analysis,each method showed a difference in accuracy. This is deemed thatsevere individual difference dropped the classification rate of thedata. Although, this study failed to show higher accuracy, it stillhas achieved that 7 different emotions compared to 3–4 emotionsby other studies, were classified. This helps lead to better chanceto recognize various human emotions using physiological signalsand it also helps lead to its application on human–computer in-teraction system based on emotions. Future studies need to obtainmore stable data to improve classification rate. In addition, thereneeds more appropriate classifiers by using SVM to obtain preciseturning.

Acknowledgement: This study is supported by the ConversingResearch Center Program through the National Research Foundationof Korea (NRF) funded by the Ministry of Education, Science andTechnology (2009-0082313).

doi:10.1016/j.ijpsycho.2011.07.003

Neural substrates involved in the processing of six differentemotional audiovisual stimuli

Mi-Sook Parka, Sunju Sohnb, Ok-Hyun Leea, Ji-A Suka,Sook-Hee Kimc, Jin-Hun Sohna

aDept. of Psychology, Brain Research Institute,Chungnam National University, Daejeon, South KoreabSchool of Social Work, University of Texas at Austin, TX, USAcArmy Substance Abuse Program, South Korea

Objective: The purpose of this study was to investigate differencesin the brain function during different emotional experiences (sad-ness, fear, anger, disgust, joy, and humor) using fMRI.

Methods: Twenty-four healthy right-handed volunteers partici-pated in the study. Six film clips were selected from movies throughour previous experiment of which purpose was to evoke differentemotional responses. There were two fMRI sessions of which had 2blocks each. Each film clip was 90 s long. Subjects were scannedduring six emotion producing conditions. Imaging was performed onan ISOL Forte 3.0 T Scanner. Single-shot EPI fMRI scans (TR/TE 3000/30 ms, Flip Angle 80, FOV 24 24 cm, Matrix Size 64 64) wereacquired. Imaging data were motion-corrected, co-registered, nor-

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International Journal of Psychophysiology

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International Journal of Psychophysiology 81 (2011) 339–342