EEG-Based Identification of Schizophrenia Using Deep Learning Techniques
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48984-6_3
Reference10 articles.
1. Ahmedt-Aristizabal, D., et al.: Identification of children at risk of schizophrenia via deep learning and EEG responses. IEEE J. Biomed. Health Inform. 25(1), 69–76 (2021). https://doi.org/10.1109/JBHI.2020.2984238
2. Roy, S., Kiral-Kornek, I., Harrer, S.: Deep learning enabled automatic abnormal EEG identification. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2756–2759 (2018). https://doi.org/10.1109/EMBC.2018.8512756
3. Wang, T., Bezerianos, A., Cichocki, A., Li, J.: Multikernel capsule network for schizophrenia identification. IEEE Trans. Cybern. 52(6), 4741–4750 (2022). https://doi.org/10.1109/TCYB.2020.3035282
4. Tanveer, M., Jatin Jangir, M.A., Ganaie, I.B., Tabish, M., Chhabra, N.: Diagnosis of Schizophrenia: a comprehensive evaluation. IEEE J. Biomed. Health Inform. 27(3), 1185–1192 (2023). https://doi.org/10.1109/JBHI.2022.3168357
5. Miller, R.L., Yaesoubi, M., Calhoun, V.D.: Cross-frequency rs-fMRI network connectivity patterns manifest differently for schizophrenia patients and healthy controls. IEEE Signal Process. Lett. 23(8), 1076–1080 (2016). https://doi.org/10.1109/LSP.2016.2585182
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