A supervised learning method for extracting and classifying EEG data

Qingxia Li; Jiamin Zhao

doi:10.1117/12.2626482

21 December 2021 A supervised learning method for extracting and classifying EEG data

Qingxia Li, Jiamin Zhao

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121560Z (2021) https://doi.org/10.1117/12.2626482
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

Electro-encephalogram (EEG) is a bioelectrical signal that directly reflects brain activity, and the research of sleep stage classifying using machine learning method is a direction of EEG signal analysis. The sleep period could be split into five stages: Wake, REM, N1, N2 and N3. At first, this essay discusses the characteristics of the Sleeping EEG wave briefly. And after the relative powers (the features) of EEG signals are extracted that are obtained by utilizing the pwelch function, the data is re-arranged with features and labels into a table. Then, the linear discrimination analysis (LDA) is used to reduce the dimension of data. Finally, the classifiers are trained with the k-nearest neighbor (KNN) classification model as well as Multiple-nominal logistic regression (MLR), respectively. The validity of the classification model is evaluated by accuracy estimating, receiver operating characteristic (ROC) curve drawing and Area under Curve (AUC) calculation.

Citation Download Citation

Qingxia Li and Jiamin Zhao "A supervised learning method for extracting and classifying EEG data", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121560Z (21 December 2021); https://doi.org/10.1117/12.2626482

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KEYWORDS

Data modeling

Electroencephalography

Feature extraction

Machine learning

Visualization

Data processing

3D modeling

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