Enhancing Brain Age Prediction: A Generative AI Approach for EEG Machine Learning Models-Reference-Cited by-同舟云学术

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Enhancing Brain Age Prediction: A Generative AI Approach for EEG Machine Learning Models

Published:2024-05-20 Issue: Volume:15 Page:1-6
ISSN:
Container-title:2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
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Author:

Cook Zara¹,Sinha Grant²,Wang Jack³,Zhao Chengzong⁴,Belacel Nabil⁵,Doesburg Sam⁶,Medvedev George⁷,Ribary Urs⁶,Vakorin Vasily⁶,Xi Pengcheng⁵

Affiliation:

1. Simon Fraser University & University of Waterloo,Waterloo,Canada

2. University of Waterloo,Computer Science,Waterloo,Canada

3. National Research Council & University of Waterloo,Waterloo,Canada

4. University of Waterloo,Computer Engineering,Waterloo,Canada

5. Digital Technologies, National Research Council Canada,Ottawa,Canada

6. Simon Fraser University,Biomedical Physiology and Kinesiology,Burnaby,Canada

7. Division of Neurology, Fraser Health Authority,Vancouver,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10560479/10560519/10560863.pdf?arnumber=10560863

Reference27 articles.

1. A review of classification algorithms for eeg-based brain-computer interfaces: a 10 year update;Lotte,2018

2. EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

3. Application of machine learning in epileptic seizure detection;Tran,2022

4. A review of epileptic seizure detection using machine learning classifiers

5. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters

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