| Abstract |
| This research investigated the classification and representation of discrete emotions based on electroencephalogram (EEG) data collected using wearable devices in response to emotional videos. To achieve this objective, a previously published dataset was reanalyzed using multivariate analyses, which are suitable for handling high-dimensional data features, minimizing information loss, and extracting core affect dimensions. First, the cross-participant classification was conducted to confirm the consistency of the emotional responses across the participants. The classification results were evaluated using a confusion matrix, indicating the correct and incorrect classifications for each emotion video. Second, based on the core affect model, multidimensional scaling (MDS) was performed to examine the representation of emotions within a two-dimensional space. Finally, representational similarity analysis (RSA) was conducted to determine the most descriptive type of data―behavioral, physiological, or EEG―in characterizing emotional dimensions. The classification analysis revealed that emotional conditions were successfully classified across participants, suggesting that the emotion-eliciting EEG response is shared across individuals. The results of MDS indicated that the EEG data collected in response to emotional stimuli exhibited clear representation along the arousal dimension. The results of RSA demonstrated that behavioral data best described the valence dimension, while EEG and physiological data best explained the arousal dimension. This study offers evidence supporting the core affect theory through EEG data collected via wearable devices in response to video stimuli. |
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| Key Words |
| Core Affect, Classification, Multidimensional Scaling, Representational Similarity Analysis, Electroencephalogram, EEG, Wearable Devices, 핵심정서, 분류분석, 다차원척도법, 표상 유사성 분석, EEG, 웨어러블 기구 |
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