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.

Faculty Mentor(s)

Razvan Andonie

Department/Program

Computer Science

hooper_machine_learning.pptx (1132 kB)
Slides for SOURCE 2019 presentation Hooper

Additional Files

hooper_machine_learning.pptx (1132 kB)
Slides for SOURCE 2019 presentation Hooper

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May 16th, 12:00 AM May 16th, 12:00 AM

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