Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals
The study demonstrates that EO rEEG can effectively distinguish between cognitive impairment and healthy states, leading to early diagnosis, cost-effective treatment, and better clinical outcomes for dementia patients. EO and EC rEEG models trained with relative PSD, particularly from parietal, temporal, occipital, and central scalp regions, can significantly assist clinicians in practice.
Authors
- Young Chul Youn (ORCID: https://orcid.org/0000-0002-2742-1759)
- Seong Soo A. An (ORCID: https://orcid.org/0000-0002-8123-6183)
- Chanda Simfukwe (ORCID: https://orcid.org/0000-0003-2845-8016)
Institutions
- Gachon University (KR)
- Chung-Ang University (KR)
Publication Details
- Journal
- Neuropsychiatric Disease and Treatment
- Published
- 2024-12-01
- DOI
- https://doi.org/10.2147/ndt.s486452
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Chung-Ang University
- Gachon University
- Ministry of Education
- National Research Foundation of Korea