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jhbmi 2020, 7(2): 214-231 |
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Safdarian N, Naji M. Detection and Classification of Emotions Using Physiological Signals and Pattern Recognition Methods. jhbmi 2020; 7 (2) :214-231
URL: http://jhbmi.ir/article-1-392-en.html
URL: http://jhbmi.ir/article-1-392-en.html
Detection and Classification of Emotions Using Physiological Signals and Pattern Recognition Methods
M.Sc. in Biomedical Engineering, Instructor, Young Researchers and Elite Club, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Abstract: (3612 Views)
Introduction: Emotions play an important role in health, communication, and interaction between humans. The ability to recognize the emotional status of people is an important indicator of health and natural relationships. In DEAP database, electroencephalogram (EEG) signals as well as environmental physiological signals related to 32 volunteers are registered. The participants in each video were rated in terms of level of arousal, capacity, liking/disliking, proficiency, and familiarity with the video they watched.
Method: In this study, a practical empirical method was adopted to classify capacity, arousal, proficiency, and interest by ranking the features extracted from signals using algorithms on EEG signals and environmental physiological signals (such as electromyography (EMG), electrooculography (EOG), galvanic skin response (GSR), respiration rate, photoplethysmography (PPG), and skin temperature. After initializing the signals from the database and pre-processing them, various features in the time and frequency domain were extracted from all signals. In this study, SVM and KNN classifiers, K-means clustering algorithm, and neural networks, such as PNN and GRNN were used to identify and classify emotions.
Results: It was indicated in this study that the results of the classification of emotions using various methods and classifiers were well-established with high accuracy. The best accuracy results were obtained by applying the proposed method using SVM classifier based on features extracted from environmental signals (85.5%) and EEG signals (82.4%).
Conclusion: According to the results of the classification of emotions in this study, the proposed algorithm provides relatively better results compared with previous similar methods.
Method: In this study, a practical empirical method was adopted to classify capacity, arousal, proficiency, and interest by ranking the features extracted from signals using algorithms on EEG signals and environmental physiological signals (such as electromyography (EMG), electrooculography (EOG), galvanic skin response (GSR), respiration rate, photoplethysmography (PPG), and skin temperature. After initializing the signals from the database and pre-processing them, various features in the time and frequency domain were extracted from all signals. In this study, SVM and KNN classifiers, K-means clustering algorithm, and neural networks, such as PNN and GRNN were used to identify and classify emotions.
Results: It was indicated in this study that the results of the classification of emotions using various methods and classifiers were well-established with high accuracy. The best accuracy results were obtained by applying the proposed method using SVM classifier based on features extracted from environmental signals (85.5%) and EEG signals (82.4%).
Conclusion: According to the results of the classification of emotions in this study, the proposed algorithm provides relatively better results compared with previous similar methods.
Keywords: Classification of Emotions, EEG Signals, Physiological Signals, Feature Extraction, Signal Processing
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2019/04/29 | Accepted: 2019/08/4
Received: 2019/04/29 | Accepted: 2019/08/4
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