Epilepsy EEG signals classification based on sparse principal component logistic regression model-Reference-Cited by-同舟云学术

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Epilepsy EEG signals classification based on sparse principal component logistic regression model

Published:2024-02-29 Issue: Volume: Page:1-9
ISSN:1025-5842
Container-title:Computer Methods in Biomechanics and Biomedical Engineering
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering

Author:

Li Xi¹,Qiao Yuanhua¹,Duan Lijuan²,Miao Jun³

Affiliation:

1. School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, China

2. Faculty of Information Technology, Beijing University of Technology, Beijing, China

3. School of Computer Science, Beijing Information Science and Technology University, Beijing, China

Funder

Beijing Municipal Education Commission and Municipal Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10255842.2024.2321991

Reference16 articles.

1. Performance exploration of multiple classifiers with grid search hyperparameter tuning for detecting epileptic seizures from EEG signals;Babu CG;J Sci Ind Res,2022

2. Hilbert Vibration Decomposition-based epileptic seizure prediction with neural network

3. Sparse logistic regression with L1/2 penalty for emotion recognition in electroencephalography classification;Chen DW;Front Neuroinf,2020

4. https://physionet.org/content/chbmit/1.0.0/.

5. Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals

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