Key result
VGGish-CNN model outperforms VGGish alone for EEG-based short-term stress detection with ~99% accuracy.
Why the study?
Human stress detection via electroencephalography (EEG) recordings during multiple tasks is a tool for promoting mental health, motivating an approach to track short-term stress experienced during mental tasks.
A VGGish-CNN deep learning model achieved 99.25% accuracy in detecting short-term stress from EEG signals during cognitive tasks.
Answers are grounded in this paper and cite their sources.
May aid acute stress research tools; leaves open clinical validation and real-world utility.
One tool for promoting mental health is human stress detection through multitasks of electroencephalography (EEG) recordings. This study proposed a short-term stress detection approach using VGGish as a feature extraction and convolution neural network (CNN) as a classifier based on EEG signals from the SAM 40 dataset. This database was recently available and was collected from 40 patients using 32 channels to identify performance on four tasks including Stroop color-word test (SCWT), answering arithmetic problems, finding mirror-identical images, and relaxing. Each task took 25 s to complete and was then repeated three times to record three trials. This means that the total EEG data contain 480 signals for four tasks recorded using 120 trials per task. The primary objective of this research was to track the amount of short-term stress that patients experienced while they engaged in the four mental tasks. Moreover, the VGGish-CNN model is applied to the SAM 40 dataset using five stages including signal preprocessing, segmentation, filtration, spectrogram, and classification process. We compared the VGGish-CNN model and the VGGish model for stress-based EEG classification to determine the best classification accuracy. The proposed approach for stress detection is the preliminary study that achieved an accuracy of 99.25% using the VGGish-CNN model on the SAM 40 dataset. Next, k-fold cross validation is performed to verify the efficiency of the VGGish-CNN model. This study can advance the application of brain–computer interface (BCI) and its use to identify patterns in EEG data that invoke stress-related inferences to aid in the diagnosis of mental disorders. In the future, investigation of human stress using EEG data will be useful in neurorehabilitation.
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Afify et al. (2025) studied Psychological stress (n=40). VGGish-CNN model vs. VGGish model was evaluated on Classification accuracy. The VGGish-CNN model achieved an accuracy of 99.25% for short-term stress detection based on EEG signals, outperforming the VGGish model alone.
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