Emotion classification
Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
16-5-2019
End Date
16-5-2019
Abstract
We present an emotion prediction system that classifies electroencephalography brain activity data into one of four emotion categories. Emotion classification is inherently difficult because of the subjective nature of emotions, thus our emotion model uses two-dimensional values of valence and arousal for classifying an individual emotional state. The EEG data was provided from the DEAP dataset, containing 40-channel EEG data from 32 participants who each watched 40, one-minute long excerpts of music videos and labeled their emotional states during each video. We demonstrate the unique challenges with working with EEG data, our methods for dimension reduction and classification, and the results we obtained using our classification model.
Recommended Citation
Hooper, Brian; Safai, Bijan; and Chandrika, Divya, "Emotion classification" (2019). Symposium Of University Research and Creative Expression (SOURCE). 192.
https://digitalcommons.cwu.edu/source/2019/Oralpres/192
Department/Program
Computer Science
Slides for SOURCE 2019 presentation Hooper
Emotion classification
Ellensburg
We present an emotion prediction system that classifies electroencephalography brain activity data into one of four emotion categories. Emotion classification is inherently difficult because of the subjective nature of emotions, thus our emotion model uses two-dimensional values of valence and arousal for classifying an individual emotional state. The EEG data was provided from the DEAP dataset, containing 40-channel EEG data from 32 participants who each watched 40, one-minute long excerpts of music videos and labeled their emotional states during each video. We demonstrate the unique challenges with working with EEG data, our methods for dimension reduction and classification, and the results we obtained using our classification model.
https://digitalcommons.cwu.edu/source/2019/Oralpres/192
Faculty Mentor(s)
Razvan Andonie