Parkinson’s Disease Classification Using Machine Learning Approaches and Resting-State EEG
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s40846-022-00695-7.pdf
Reference30 articles.
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2. Belardinelli, P., Azodi-Avval, R., Ortiz, E., Naros, G., Grimm, F., Weiss, D., & Gharabaghi, A. (2019). Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson’s disease. Journal of Neurosurgery, 132(4), 1234–1242. https://doi.org/10.3171/2018.11.JNS181684
3. Betrouni, N., Delval, A., Chaton, L., Defebvre, L., Duits, A., Moonen, A. … Dujardin, K. (2019). Electroencephalography-based machine learning for cognitive profiling in Parkinson’s disease: Preliminary results. Movement Disorders, 34(2), 210–217. https://doi.org/10.1002/mds.27528
4. Blesa, J., Trigo-Damas, I., Dileone, M., Del Rey, N. L., Hernandez, L. F., & Obeso, J. A. (2017). Compensatory mechanisms in Parkinson’s disease: Circuits adaptations and role in disease modification. Experimental Neurology, 298(Pt B), 148–161. https://doi.org/10.1016/j.expneurol.2017.10.002
5. Boonstra, T. W., Nikolin, S., Meisener, A. C., Martin, D. M., & Loo, C. K. (2016). Change in mean frequency of resting-state electroencephalography after transcranial direct current stimulation. Frontiers in Human Neuroscience, 10, 270. https://doi.org/10.3389/fnhum.2016.00270
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