Analysis of Personality and EEG Features in Emotion Recognition Using Machine Learning Techniques to Classify Arousal and Valance Labels
| Autor: | MARTINEZ, TEJADA Laura Alejandra, Martinez Tejada, Laura Alejandra, Maruyama, Yasuhisa, Yoshimura, Natsue, Koike, Yasuharu |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Computer engineering. Computer hardware
media_common.quotation_subject False positives and false negatives human computer interactions lcsh:TK7885-7895 050109 social psychology Feature selection 02 engineering and technology Electroencephalography Machine learning computer.software_genre Arousal feature selection emotion recognition 0202 electrical engineering electronic engineering information engineering medicine Personality 0501 psychology and cognitive sciences Emotion recognition EEG Big Five personality traits Valence (psychology) media_common medicine.diagnostic_test business.industry 05 social sciences machine learning personality 020201 artificial intelligence & image processing Artificial intelligence business Psychology computer psychological phenomena and processes |
| Zdroj: | Machine Learning and Knowledge Extraction Volume 2 Issue 2 Pages 7-124 Machine Learning and Knowledge Extraction, Vol 2, Iss 7, Pp 99-124 (2020) |
| ISSN: | 2504-4990 |
| DOI: | 10.3390/make2020007 |
| Popis: | We analyzed the contribution of electroencephalogram (EEG) data, age, sex, and personality traits to emotion recognition processes&mdash through the classification of arousal, valence, and discrete emotions labels&mdash using feature selection techniques and machine learning classifiers. EEG traits and age, sex, and personality traits were retrieved from a well-known dataset&mdash AMIGOS&mdash and two sets of traits were built to analyze the classification performance. We found that age, sex, and personality traits were not significantly associated with the classification of arousal, valence and discrete emotions using machine learning. The added EEG features increased the classification accuracies (compared with the original report), for arousal and valence labels. Classification of arousal and valence labels achieved higher than chance levels however, they did not exceed 70% accuracy in the different tested scenarios. For discrete emotions, the mean accuracies and the mean area under the curve scores were higher than chance however, F1 scores were low, implying that several false positives and false negatives were present. This study highlights the performance of EEG traits, age, sex, and personality traits using emotion classifiers. These findings could help to understand the traits relationship in a technological and data level for personalized human-computer interactions systems. |
| Databáze: | OpenAIRE |
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