Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention

TL;DR

Brain2Vec is a novel deep learning framework combining CNN, LSTM, and attention mechanisms to classify stress states from EEG signals, showing promising accuracy and potential for wearable health applications.

Contribution

The paper introduces Brain2Vec, a hybrid deep learning model that effectively captures spatial, temporal, and important features in EEG data for stress detection, advancing non-invasive diagnostic tools.

Findings

Achieved an AUC of 0.68 on DEAP dataset

Validated accuracy of 81.25% in stress classification

Demonstrated potential for wearable stress monitoring systems

Abstract

Mental stress has become a pervasive factor affecting cognitive health and overall well-being, necessitating the development of robust, non-invasive diagnostic tools. Electroencephalogram (EEG) signals provide a direct window into neural activity, yet their non-stationary and high-dimensional nature poses significant modeling challenges. Here we introduce Brain2Vec, a new deep learning tool that classifies stress states from raw EEG recordings using a hybrid architecture of convolutional, recurrent, and attention mechanisms. The model begins with a series of convolutional layers to capture localized spatial dependencies, followed by an LSTM layer to model sequential temporal patterns, and concludes with an attention mechanism to emphasize informative temporal regions. We evaluate Brain2Vec on the DEAP dataset, applying bandpass filtering, z-score normalization, and epoch segmentation as part of a comprehensive preprocessing pipeline. Compared to traditional CNN-LSTM baselines, our proposed model achieves an AUC score of 0.68 and a validation accuracy of 81.25%. These findings demonstrate Brain2Vec's potential for integration into wearable stress monitoring platforms and personalized healthcare systems.