Presentation Title

A Comparative Analysis of Machine Learning Techniques for Emotion Recognition using EEG and Peripheral Physiological Signals

Faculty Mentor

Dr. Matin Pirouz

Start Date

23-11-2019 10:45 AM

End Date

23-11-2019 11:30 AM

Location

166

Session

poster 4

Type of Presentation

Poster

Subject Area

engineering_computer_science

Abstract

Emotion recognition using brain signals has the potential to change the way we identify and treat some health conditions. Difficulties and limitations may arise in general emotion recognition software due to the restricted number of facial expression triggers, dissembling of emotions, or among people with alexithymia. Such triggers are identified by studying the continuous brainwaves generated by human brain. Electroencephalogram (EEG) signals from the brain give us a more diverse insight on emotional states that one may not be able to express. Brainwave EEG signals can reflect the changes in electrical potential resulting from communications networks between neurons. This research involves analyzing the epoch data from EEG sensor channels and performing comparative analysis of multiple machine learning techniques: Principal component analysis (PCA) coupled with Grid-search, Support Vector Machine (SVM), K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression and Decision Trees. It is found out that EEG data is a viable alternative to detect emotions that one may not be able express or admit such as stress or pride. The DEAP Dataset is used for this study, which is a multimodal dataset for the analysis of human affective states. The dataset contains 40 one-minute long excerpts of music videos, and participants rated each video in terms of the level of arousal, valence, like/dislike, dominance and familiarity. The best generalized automated classification technique is identified. PCA with SVM performed the best and produced an F1-score of 84.73% with 98.01% recall in the 30th to 45th interval of segmentation, demonstrating the effectiveness of such machine learning techniques.

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Nov 23rd, 10:45 AM Nov 23rd, 11:30 AM

A Comparative Analysis of Machine Learning Techniques for Emotion Recognition using EEG and Peripheral Physiological Signals

166

Emotion recognition using brain signals has the potential to change the way we identify and treat some health conditions. Difficulties and limitations may arise in general emotion recognition software due to the restricted number of facial expression triggers, dissembling of emotions, or among people with alexithymia. Such triggers are identified by studying the continuous brainwaves generated by human brain. Electroencephalogram (EEG) signals from the brain give us a more diverse insight on emotional states that one may not be able to express. Brainwave EEG signals can reflect the changes in electrical potential resulting from communications networks between neurons. This research involves analyzing the epoch data from EEG sensor channels and performing comparative analysis of multiple machine learning techniques: Principal component analysis (PCA) coupled with Grid-search, Support Vector Machine (SVM), K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression and Decision Trees. It is found out that EEG data is a viable alternative to detect emotions that one may not be able express or admit such as stress or pride. The DEAP Dataset is used for this study, which is a multimodal dataset for the analysis of human affective states. The dataset contains 40 one-minute long excerpts of music videos, and participants rated each video in terms of the level of arousal, valence, like/dislike, dominance and familiarity. The best generalized automated classification technique is identified. PCA with SVM performed the best and produced an F1-score of 84.73% with 98.01% recall in the 30th to 45th interval of segmentation, demonstrating the effectiveness of such machine learning techniques.