On-Chip Mental Stress Detection: Integrating a Wearable Behind-The-Ear EEG Device With Embedded Tiny Neural Network

Abstract

The study introduces an innovative approach to efficient mental stress detection by combining electroencephalography (EEG) analysis with on-chip neural networks, taking advantage of EEG's temporal resolution and the computational capabilities of embedded neural networks. The proposed system utilizes behind-the-ear (BTE) EEG signals and on-chip neural networks for mental stress detection. A wearable custom-designed device captures EEG signals from a single BTE channel, performs on-chip signal-to-spectrogram conversion, and integrates a compact convolutional neural network (CNN) for stress classification. The system systematically identifies key EEG frequency bands associated with stress and includes a user-friendly smartphone application for intuitive stress monitoring. EEG data were collected from 15 participants during stress-inducing tasks, such as Stroop and Mental Arithmetic tests. On-chip processing is essential for filtering EEG noise, converting signals into spectrogram images, and using these images as inputs for stress detection through the proposed on-chip CNN model. The experimental results demonstrate strong performance: leave-one-out cross-validation (LOOCV) achieves 91.72% accuracy, 93.74% specificity, 88.69% sensitivity, 90.43% precision, and an F1-score of 0.8955; while 10-fold cross-validation (CV) yields 95.32% accuracy, 95.89% specificity, 94.47% sensitivity, 93.95% precision, and an F1-score of 0.9421 on untrained datasets. The Beta band (13 Hz to 30 Hz) is identified as the most significant frequency band for detecting mental stress. This integration of BTE EEG analysis with on-chip CNNs represents a significant advancement in mental stress detection and has potential applications in medical assistance tools.

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