Cerebro Wave Bee transformer to leverage EEG based hand movement classification using optimized bio-inspired learning rates
N Priyadharshini Jayadurga, M Chandralekha, Hema Subramaniam · 2026
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Description
Priyadharshini Jayadurga, Chandralekha, and Hema Subramaniam published a neurotechnology study in twenty twenty-six on classifying hand movements from brain waves. EEG-based control could drive assistive devices, but transformer models are touchy about their learning rate. The team let a bee-inspired optimizer choose it before training.
Honey Bee Optimization searched for the best learning rate offline, so the transformer trained stably from the start. The resulting system, nicknamed Cerebro Wave Bee, reached about ninety-five point six percent classification accuracy. The authors argue bio-inspired tuning makes deep models more reliable on fickle brain signals. Applications span medical signal analysis and hands-free control for people with limited movement.
Catalog record
| CREATOR | N Priyadharshini Jayadurga, M Chandralekha, Hema Subramaniam |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 79 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |