Multi-Class Emotion Classification with XGBoost Model Using Wearable EEG Headband Data

Authors

James Khamthung, Southern Methodist UniversityFollow
Nibhrat Lohia, Southern Methodist UniversityFollow
Seement Srivastava, Southern Methodist UniversityFollow

Abstract

Electroencephalography (EEG) or brainwave signals serve as a valuable source for discerning human activities, thoughts, and emotions. This study explores the efficacy of EXtreme Gradient Boosting (XGBoost) models in sentiment classification using EEG signals, specifically those captured by the MUSE EEG headband. The MUSE device, equipped with four EEG electrodes (TP9, AF7, AF8, TP10), offers a cost-effective alternative to traditional EEG setups, which often utilize over 60 channels in laboratory-grade settings. Leveraging a dataset from previous MUSE research (Bird, J. et al., 2019), emotional states (positive, neutral, and negative) were observed in a male and a female participant, each for 3 minutes per state while watching movie scenes designed to stimulate emotions. The dataset comprises 2548 features extracted statistically from each sliding time window (mean, median, standard deviation, etc.). Employing XGBoost, a subset of the top 100 features is selected from the original 2548, achieving an exceptional accuracy of 99.1%. This research aims to make significant contributions to accurately classify human emotion while advancing EEG-based sentiment classification for future real-time emotion prediction applications.

Recommended Citation

Khamthung, James; Lohia, Nibhrat; and Srivastava, Seement (2024) "Multi-Class Emotion Classification with XGBoost Model Using Wearable EEG Headband Data," SMU Data Science Review: Vol. 8: No. 1, Article 7.
Available at: https://scholar.smu.edu/datasciencereview/vol8/iss1/7