Evidence of emotion-antecedent appraisal checks in electroencephalography and facial electromyography
| Autor: | Klaus R. Scherer, Eduardo Coutinho, Kornelia Gentsch, Björn Schuller, J.M. van Peer |
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| Rok vydání: | 2018 |
| Předmět: |
Support Vector Machine
Computer science Physiology Interface (computing) Emotions Social Sciences lcsh:Medicine Electromyography Electroencephalography Experimental Psychopathology and Treatment 0302 clinical medicine Cognition Medicine and Health Sciences Psychology lcsh:Science Clinical Neurophysiology Brain Mapping Numerical Analysis Multidisciplinary medicine.diagnostic_test 05 social sciences Novelty Brain Electrophysiology Signal Filtering Bioassays and Physiological Analysis Brain Electrophysiology Physical Sciences Engineering and Technology Anatomy Facial electromyography Muscle Electrophysiology Research Article Process (engineering) Imaging Techniques General Science & Technology Neurophysiology Neuroimaging Research and Analysis Methods 050105 experimental psychology 03 medical and health sciences MD Multidisciplinary medicine Humans 0501 psychology and cognitive sciences Scalp business.industry Electrophysiological Techniques lcsh:R Biology and Life Sciences Pattern recognition Models Theoretical Interpolation Support vector machine Face Signal Processing Cognitive Science lcsh:Q Artificial intelligence Clinical Medicine ddc:004 business Head 030217 neurology & neurosurgery Mathematics Neuroscience |
| Zdroj: | PLoS One, 13, 1 PLoS One PLoS ONE, Vol 13, Iss 1, p e0189367 (2018) PLoS ONE PLoS One, 13 |
| ISSN: | 1932-6203 |
| Popis: | Contains fulltext : 181132.pdf (Publisher’s version ) (Open Access) In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks - novelty, intrinsic pleasantness, goal conduciveness, control, and power - in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two data sets obtained from previous studies. Our results show that novelty and power appraisal checks can be consistently detected in EEG signals above chance level (binary tasks). For novelty, the best classification performance in terms of accuracy was achieved using features extracted from the whole scalp, and by averaging across 20 individual trials in the same experimental condition (UAR = 83.5 ± 4.2; N = 25). For power, the best performance was obtained by using the signals from four pre-selected EEG channels averaged across all trials available for each participant (UAR = 70.6 ± 5.3; N = 24). Together, our results indicate that accurate classification can be achieved with a relatively small number of trials and channels, but that averaging across a larger number of individual trials is beneficial for the classification for both appraisal checks. We were not able to detect any evidence of the appraisal checks under study in the EMG data. The proposed methodology is a promising tool for the study of the psychophysiological mechanisms underlying emotional episodes, and their application to the development of computerized tools (e.g., Brain-Computer Interface) for the study of cognitive processes involved in emotions. 19 p. |
| Databáze: | OpenAIRE |
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