Feature fusion and WOA-GWO optimization for Alzheimer’s disease detection with sparse EEG channels
Ruofan Wang, Jitong Wang, Jiaxuan Cai, Siqian Wang, Zixuan Bai, Yanqiu Che · 2026
Read Feature fusion and WOA-GWO optimization for Alzheimer’s disease detection with sparse EEG channels by Ruofan Wang, Jitong Wang, Jiaxuan Cai, Siqian Wang, Zixuan Bai, Yanqiu Che (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13194138.txt.
Description
Ruofan Wang and five co-authors aim to spot Alzheimer's disease from a few scalp sensors in this two thousand twenty-six paper. The disease creeps in slowly, so early warning is hard. Brain wave recordings are cheap and painless, but full sensor caps are heavy to process. The team built a method that picks a small set of useful channels. It mixes shape features and complexity measures from the signals.
A hybrid search algorithm, part whale and part wolf pack in style, chooses the channels. Tweaks like chaotic starts improve the search. Strong accuracy with sparse sensors would suit clinics without big budgets. The work serves teams building screening aids, not final diagnoses. It also shows how smart channel choice cuts computing cost.
Catalog record
| CREATOR | Ruofan Wang, Jitong Wang, Jiaxuan Cai, Siqian Wang, Zixuan Bai, Yanqiu Che |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 103 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |