Machine learning methods in predicting electroencephalogram

Zizhao Lin; Yijiang Ma

doi:10.1117/12.2626522

21 December 2021 Machine learning methods in predicting electroencephalogram

Zizhao Lin, Yijiang Ma

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 1215615 (2021) https://doi.org/10.1117/12.2626522
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

Sleep is an essential physiological activity. Previous work not only uses traditional biological methods but also uses computer technology as an aid. Machine learning can process and analyze the known data and then predict the unknown data scientifically. The high accuracy of the prediction results makes people hope to use machine learning in medical research. The research content of this paper is to preprocess and classify EEG data sets. We use Support Vector Machine (SVM), Random Forest, and Multi-Layer Perceptron (MLP) to classify the processed data sets and get their corresponding results and accuracy, respectively. This study found that the deep learning method is not a good choice because of the small amount of data.

Citation Download Citation

Zizhao Lin and Yijiang Ma "Machine learning methods in predicting electroencephalogram", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 1215615 (21 December 2021); https://doi.org/10.1117/12.2626522

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