Evaluating methods of oversampling and averaging resting-state electroencephalography data in classifying Parkinson’s disease <sup>*</sup>-Reference-Cited by-同舟云学术

Evaluating methods of oversampling and averaging resting-state electroencephalography data in classifying Parkinson’s disease ^*

Published:2023-07-24 Issue: Volume: Page:
ISSN:
Container-title:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Vlieger Robin¹,Suominen Hanna²,Apthorp Deborah³,Lueck Christian J.⁴,Daskalaki Elena¹

Affiliation:

1. Australian National University,School of Computing, College of Engineering, Computing and Cybernetics,Canberra,Australia,ACT 2601

2. College of Health and Medicine, Australian National University,School of Computing, College of Engineering, Computing and Cybernetics, and the School of Medicine and Psychology,Canberra,Australia,ACT 2601

3. University of New England,School of Psychology, Faculty of Medicine and Health,Armidale,Australia,NSW 2350

4. Australian National University,School of Medicine and Psychology, College of Health and Medicine,Canberra,Australia,ACT 2601

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10339936/10339939/10340819.pdf?arnumber=10340819

Reference31 articles.

1. Machine Learning Approaches for Detecting Parkinson’s Disease from EEG Analysis: A Systematic Review

2. Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

3. On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics

5. Volume conduction and EEG measurements within the brain: A quantitative approach to the influence of electrical spread on the linear relationship of activity measured at different locations