Stress Detection Using Novel Time–Frequency Decomposition: Progressive Fourier Transform

Stress is a natural reaction to challenges encountered in everyday life. Chronic stress, which lasts for a long time, can negatively influence mental and physical health. Therefore, early detection and assessment of stress are crucial to reducing the risk of harm to an individual’s well-being. Electroencephalograph (EEG) brain signals can be used to assess human stress levels. This research aims to investigate how EEG signals can detect stress using deep learning based on a new feature extraction technique. We proposed new feature decomposition approaches based on the progressive Fourier transform and the coordination of multiple brain areas working simultaneously. Convolutional neural networks (CNNs) were employed in our study to extract and classify stress features captured from the image representations of EEG signals. The performance of the proposed method was evaluated on publicly available EEG dataset. Our experiment results demonstrated that our proposed method outperformed previous studies in detecting different mental states. The progressive Fourier transformation yielded the highest accuracy of 97.9% in classifying three mental states (Concentrating/Neutral/Relaxed) when conducting tenfolds cross validation using the AlexNet model.