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A novel technique for stress detection from EEG signal using hybrid deep learning model

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Abstract

Stress is burgeoning in today’s fast-paced lifestyle, and its detection is imperative. An electroencephalography (EEG) technique is used to identify the brain’s activities from the brain’s electrical bio-signals. However, only a highly trained physician can elucidate EEG signals due to their complexity. This study proposes a DWT-based hybrid deep learning model based on Convolution Neural Network and Bidirectional Long Short-Term Memory (CNN–BLSTM), which detects stress levels in humans. Further supports neurologists, mental health counselors, and physicians in making decisions on stress levels. The Physionet EEG dataset is used to detect the stress level for mental arithmetic tasks. Noise from multi-channel (19 channels) EEG signals has been removed and decomposed into four levels using Discrete Wavelet Transform (DWT). After decomposition, an automatic feature selection method, namely Convolution Neural Network (CNN), is used on the decomposed signals. Finally, BLSTM is used to classify stress levels. The accuracy of the proposed model is compared with CNN-based Long Short-Term Memory (LSTM) and previous work. The results show that the proposed hybrid model achieved higher classification accuracy (99.20%) compared to others. Further, the stratified tenfold cross-validation technique is applied to validate the proposed model with a classification accuracy of 98.10%.

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Authors and Affiliations

  1. School of Computing Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India

    Lokesh Malviya & Sandip Mal

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  1. Lokesh Malviya
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  2. Sandip Mal
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Correspondence to Sandip Mal.

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Author Lokesh Malviya declares that he has no conflict of interest. Author Sandip Mal declares that he has no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Malviya, L., Mal, S. A novel technique for stress detection from EEG signal using hybrid deep learning model. Neural Comput & Applic 34, 19819–19830 (2022). https://doi.org/10.1007/s00521-022-07540-7

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