Explainable machine learning for Alzheimer's disease characterization using small-sample EEG data
Lang Shen, Wei Tong, Ye Zhao, Bicheng Wu, Peng Zhang, Jing Kan · 2026
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Description
Brain wave recordings are cheap and painless, but reading Alzheimer's disease from them takes care and clear reasoning. Lang Shen and five co-authors built such a reader in 2026, tested on recordings from sixty-three people, thirty-six with the disease and twenty-seven healthy. Their method checks each person as a whole and explains which wave traits drive its calls.
The chosen model sorted the groups with strong accuracy, and repeat runs held steady. The traits it leaned on included calm rhythm strength at the back of the head and slow wave power at the front and sides. A second, model free look at brain states also differed between the groups. The authors call the results promising and ask for larger, separate tests.
Catalog record
| CREATOR | Lang Shen, Wei Tong, Ye Zhao, Bicheng Wu, Peng Zhang, Jing Kan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 65 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |