Key result
ResNet50 transfer learning on EEG spectrograms beats existing methods with ~96% two-level stress classification accuracy.
Why the study?
Stress impacts physical and mental well-being and can lead to cardiovascular diseases, motivating novel methods to classify perceived mental stress using EEG signals.
Does a transfer learning-based framework using EEG spectrograms improve the classification accuracy of perceived mental stress?
Does a transfer learning-based framework using EEG spectrograms improve the classification accuracy of perceived mental stress?
A transfer learning framework using ResNet50 on STFT-generated EEG spectrograms achieved high accuracy in classifying perceived mental stress levels.
Answers are grounded in this paper and cite their sources.
Supports feasibility of EEG-based stress classification; leaves open prospective clinical validation before adoption.
Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals.We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies.
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Khan et al. (2025) studied Perceived mental stress (n=53). Transfer learning with ResNet50 on STFT-generated EEG spectrograms vs. Existing state-of-the-art methods was evaluated on Two-level stress classification accuracy. A transfer learning framework using ResNet50 on STFT-generated EEG spectrograms achieved superior classification accuracies of 95.80% and 86.02% for two- and three-level stress classification.
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