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Quadrature Response Spectra Deep Neural Based Behavioral Pattern Analytics for Epileptic Seizure Identification


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Wen, D., Li, R., Tang, H., Liu, Y., Wan, X., Dong, X., Saripan, M. I., Lan, X., Song, H., Zhou, Y. (2022). Task-state EEG signal classification for spatial cognitive evaluation based on multiscale high-density convolutional neural network. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 1041-1051. https://doi.org/10.1109/tnsre.2022.3166224 Search in Google Scholar

Jo, S.-Y., Jeong, J.-W. (2020). Prediction of visual memorability with EEG signals: A comparative study. Sensors, 20 (9), 2694. https://doi.org/10.3390%2Fs20092694 Search in Google Scholar

Diachenko, M., Houtman, S. J., Juarez-Martinez, E. L., Ramautar, J. R., Weiler, R., Mansvelder, H. D., Bruining, H., Bloem, P., Linkenkaer-Hansen, K. (2022). Improved manual annotation of EEG signals through convolutional neural network guidance. eNeuro, 9 (5). https://doi.org/10.1523/eneuro.0160-22.2022 Search in Google Scholar

Arı, E., Taçgın, E. (2023). Input shape effect on classification performance of raw EEG motor imagery signals with convolutional neural networks for use in brain-computer interfaces. Brain Sciences, 13 (2), 240. https://doi.org/10.3390%2Fbrainsci13020240 Search in Google Scholar

Craik, A., He, Y., Contreras-Vidal, J. L. (2019). Deep learning for electroencephalogram (EEG) classification tasks: A review. Journal of Neural Engineering, 16 (3), 031001. https://doi.org/10.1088/1741-2552/ab0ab5 Search in Google Scholar

Craik, A., González-España, J. J., Alamir, A., Edquilang, D., Wong, S., Sánchez Rodríguez, L., Feng, J., Francisco, G. E., Contreras-Vidal, J. L. (2023). Design and validation of a low-cost mobile EEG-based Brain-Computer Interface. Sensors, 23 (13), 5930. https://doi.org/10.3390/s23135930 Search in Google Scholar

Shoeibi, A., Sadeghi. D., Moridian. P., Ghassemi, N., Heras, J., Alizadehsani, R., Khadem, A., Kong, Y., Nahavandi, S., Zhang, Y. D., Gorriz, J. M. (2021). Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models. Frontiers in Neuroinformatics, 15, 777977. https://doi.org/10.3389/fninf.2021.777977 Search in Google Scholar

Hosseini, M.-P., Hosseini, A., Ahi, K. (2021). A review on machine learning for EEG signal processing in bioengineering. IEEE Reviews in Biomedical Engineering, 14, 204-218. https://doi.org/10.1109/rbme.2020.2969915 Search in Google Scholar

Thangarajoo, R. G., Reaz, M. B. I., Srivastava, G., Haque, F., Ali, S. H. M., Bakar, A. A. A., Bhuiyan, M. A. S. (2021). Machine learning-based epileptic seizure detection methods using wavelet and EMD-based decomposition techniques: A review. Sensors, 21 (24), 8485. https://doi.org/10.3390/s21248485 Search in Google Scholar

Parameswari, A., Vinoth Kumar, K., Gopinath, S., (2022). Thermal analysis of Alzheimer’s disease prediction using random forest classification model. Materials Today: Proceedings, 66 (3), 815-821. https://doi.org/10.1016/j.matpr.2022.04.357 Search in Google Scholar

eISSN:
1335-8871
Language:
English
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6 times per year
Journal Subjects:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing