Depression Recognition Based on Pre-trained ResNet-18 Model and Brain Effective Connectivity Network
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3626-3_22
Reference34 articles.
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2. Afshani, F., Shalbaf, A., Shalbaf, R., Sleigh, J.: Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia. Cogn. Neurodyn. 13, 531–540 (2019)
3. Akbari, H., et al.: Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features. Appl. Acoust. 179, 108078 (2021)
4. Ay, B., et al.: Automated depression detection using deep representation and sequence learning with EEG signals. J. Med. Syst. 43, 1–12 (2019)
5. Bagherzadeh, S., Shahabi, M.S., Shalbaf, A.: Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput. Biol. Med. 146, 105570 (2022)
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