Ilias Naros
Papers
1
Total Citations
82
H-Index
1
About
Ilias Naros is a leading researcher in the intersection of computational neuroscience and motor rehabilitation, with a primary focus on how the brain’s oscillatory dynamics—particularly beta-band rhythms—can be harnessed to improve motor control. His most influential work, “Reinforcement learning of self-regulated sensorimotor β-oscillations improves motor performance” (2016, 82 citations), demonstrates a groundbreaking approach: using real-time neurofeedback and reinforcement learning to train individuals to voluntarily modulate their own sensorimotor beta oscillations. This study not only provided a direct causal link between beta activity and motor performance but also opened new avenues for brain-computer interface (BCI) therapies in stroke and Parkinson’s disease. Naros’s contributions have been pivotal in translating basic neural oscillation research into practical, closed-loop rehabilitation tools. With over 80 citations on this single paper alone, his work is widely recognized for its methodological innovation and clinical potential. By bridging reinforcement learning algorithms with neurophysiology, Naros has established a framework that empowers patients to actively reshape their neural activity, marking a significant step toward personalized, brain-based motor recovery.
Research Focus
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Top Papers
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