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Maksimenko, V.A., Kurkin, S.A., Pitsik, E.N., Musatov, V.Yu., Runnova, A.E., Efremova, T.Yu., Hramov, A.E. and Pisarchik, A.N. (2018) Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity. Complexity, 2018.
https://doi.org/10.1155/2018/9385947

has been cited by the following article:

• TITLE: Applying Neural Network in Classifying Parkinson’s Disease

  AUTHORS: Yuan-Liang (Tom) Liao

  KEYWORDS: Machine Learning, Linear Algebra, Error Analysis, Neural Network

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.10, October 27, 2020

  ABSTRACT: This project uses knowledge of neural network to analyze if the person under study is analyzed to be Parkinson disease patient or not. Binary classification is constructed based on the multi-feature database. A decision boundary is clearly plotted to separate patient with and without Parkinson disease. Results show that over 80% accuracy could be obtained with the preliminary results. Future efforts could be performed to construct more complicated neural network to improve the accuracy.