The underlying mechanisms of tDCS-Based cognitive enhancement in unpredictable task switching: A machine learning-based approach for EEG signal analysis
Ziyu Wang, Ziyuan Zhang, Rongjuan Zhu, Xinru Zhang, Xuqun You, Jimin Liang · 2026
Read The underlying mechanisms of tDCS-Based cognitive enhancement in unpredictable task switching: A machine learning-based approach for EEG signal analysis by Ziyu Wang, Ziyuan Zhang, Rongjuan Zhu, Xinru Zhang, Xuqun You, Jimin Liang (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/PMC13427431.txt.
Description
Ziyu Wang and coauthors probed how brain stimulation sharpens task switching using EEG and machine learning in a 2026 double-blind study of 24 participants. Volunteers performed unpredictable magnitude and parity switching tasks under anodal stimulation over the right dorsolateral prefrontal cortex or sham stimulation. Active stimulation improved switch cost accuracy while reducing mixing cost accuracy, a split behavioral pattern needing neural explanation.
Brain wave analysis found larger N1 responses in repeat trials. It also erased N2 latency differences between switch and repeat trials. Power in alpha, beta, and theta bands shifted in specific windows, with central theta gains in women. Machine learning classification helped separate responders, addressing inconsistent stimulation outcomes. Brain waves were read in fine time slices, so the team could track how the pulse shifts thought step by step as each task switch came and went.
Catalog record
| CREATOR | Ziyu Wang, Ziyuan Zhang, Rongjuan Zhu, Xinru Zhang, Xuqun You, Jimin Liang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 56873 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |