Functional connectivity associated with severe upper limb impairment in resting-state electroencephalography among chronic stroke survivors: a machine learning approach
Ji-Yoon Lee, Miseon Shim, Won Kee Chang, Hee-Mun Cho, Ji-Soo Choi, Hyunji Kim, Bongwon Suh, Nam-Jong Paik, Han-Jeong Hwang, Won-Seok Kim · 2025
Read Functional connectivity associated with severe upper limb impairment in resting-state electroencephalography among chronic stroke survivors: a machine learning approach by Ji-Yoon Lee, Miseon Shim, Won Kee Chang, Hee-Mun Cho, Ji-Soo Choi, Hyunji Kim, Bongwon Suh, Nam-Jong Paik, Han-Jeong Hwang, Won-Seok Kim (2025) 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-12751579.txt.
Description
Ji-Yoon Lee and nine co-authors published a study in 2025 on brain wave patterns that mark severe arm weakness after stroke. Survivors whose arm barely recovers face a long, uncertain rehabilitation. A resting brain recording that flags severe cases early could guide therapy choices and honest expectations, so the team mined such recordings with machine learning.
They studied thirty-four chronic stroke survivors, split by a standard arm score into milder and severe groups. Links between brain sites were computed across six wave bands for both halves of the brain. One lean model, pared to fourteen features, sorted severe from milder cases with about ninety-one percent balanced accuracy. A short, painless recording may carry more forecast power than clinics currently use.
Catalog record
| CREATOR | Ji-Yoon Lee, Miseon Shim, Won Kee Chang, Hee-Mun Cho, Ji-Soo Choi, Hyunji Kim, Bongwon Suh, Nam-Jong Paik, Han-Jeong Hwang, Won-Seok Kim |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |