Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks
Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10328579/ |
| _version_ | 1797392271409152000 |
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| author | A. Sam R. Boostani S. Hashempour M. Taghavi S. Sanei |
| author_facet | A. Sam R. Boostani S. Hashempour M. Taghavi S. Sanei |
| author_sort | A. Sam |
| collection | DOAJ |
| description | Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain’s underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional brain-template structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods. |
| first_indexed | 2024-03-08T23:44:50Z |
| format | Article |
| id | doaj.art-8b477578566349f6805bcc390205a975 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-08T23:44:50Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-8b477578566349f6805bcc390205a9752023-12-14T00:00:14ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01314725473710.1109/TNSRE.2023.333646710328579Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural NetworksA. Sam0https://orcid.org/0009-0005-1109-8853R. Boostani1https://orcid.org/0000-0003-0055-4452S. Hashempour2https://orcid.org/0000-0003-3613-3107M. Taghavi3S. Sanei4https://orcid.org/0000-0002-1446-5744CSE&IT Department of Electrical and Computer Engineering, Shiraz University, Shiraz, IranCSE&IT Department of Electrical and Computer Engineering, Shiraz University, Shiraz, IranCSE&IT Department of Electrical and Computer Engineering, Shiraz University, Shiraz, IranDepartment of Psychiatry, School of Medicine, Islamic Azad University, Kazerun Branch, Kazerun, IranDepartment of Electrical and Electronic Engineering, Imperial College London, London, U.K.Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain’s underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional brain-template structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.https://ieeexplore.ieee.org/document/10328579/Beck depression inventoryLSTMspiking neural networksynaptic time dependent plasticity |
| spellingShingle | A. Sam R. Boostani S. Hashempour M. Taghavi S. Sanei Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks IEEE Transactions on Neural Systems and Rehabilitation Engineering Beck depression inventory LSTM spiking neural network synaptic time dependent plasticity |
| title | Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks |
| title_full | Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks |
| title_fullStr | Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks |
| title_full_unstemmed | Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks |
| title_short | Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks |
| title_sort | depression identification using eeg signals via a hybrid of lstm and spiking neural networks |
| topic | Beck depression inventory LSTM spiking neural network synaptic time dependent plasticity |
| url | https://ieeexplore.ieee.org/document/10328579/ |
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