Work

Contribution of EEG Signals for Students’ Stress Detection

Year: 2024

Type: article

Abstract: Stress is a prevalent global concern impacting individuals across various life aspects. This paper investigates stress detection using electroencephalographic (EEG) signals, which have proven valuable for studying neural correlates of stress. Stress was induced in students, and physiological data was recorded as part of the experimental setup. Different feature sets were extracted and four machine learning models, including LightGBM, Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), were utilized for classification tasks. The findings indicate that the mean and standard deviation of 19 channels consistently outperform other feature sets. LightGBM demonstrates superior performance across all scenarios compared to CNN, KNN, and SVM. Overall, this study presents an effective stress detection approach using EEG signals and demonstrates the potential of integrating simple statistical features for enhanced classification accuracy. The findings contribute to the advancement of stress monitoring technologies, with potential applications in wearables and BCIs for real-time stress management.

Source: IEEE Transactions on Affective Computing

DOI: 10.1109/taffc.2024.3503995

Authors: Jonah Fernandez, Raquel Martínez, Bianca Innocenti, Beatriz López

Institutions: Universitat de Girona, University of the Basque Country

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,495