EEG-Based Human Stress Level Predictor Using Customized EEGNet Model

Abstract

The increasing interest in Electro-encephalogram (EEG)-based stress prediction is driven by the global prevalence of stress. However, current studies predominantly rely on machine learning and deep learning techniques, utilizing extensive EEG data from 8 to 32 channels for stress prediction. In contrast, our research proposes an innovative approach that predicts stress using only 2 EEG channels and focuses on a specific frequency band (beta). The dataset used in this work is collected and pre-processed in a novel approach which is discussed in depth. Moreover, we have transformed the entire system into a TFLite model to enhance portability. Our experimental results, conducted on 10 subjects, demonstrate that our proposed technique achieves a remarkable prediction accuracy of 74%. Notably, this performance is comparable to other models that employ up to 128-channel data and consider multiple frequency bands. Our work lays the foundation for future advancements, with the ultimate goal of developing a portable EEG-based headband featuring only 2 channels. This would enable stress prediction, and the results could be easily accessed through either a mobile or web interface. By streamlining the EEG data acquisition and focusing on a specific frequency band, our approach not only achieves impressive prediction accuracy but also paves the way for the development of more user-friendly and accessible stress prediction technologies. This has the potential to significantly impact stress management and well-being on a global scale.