Parviz Shahabi

University of Tabriz, Tabriz University of Medical Sciences

Papers

Total Citations

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About

Parviz Shahabi is a pioneering researcher at the intersection of computational intelligence, brain-computer interfaces (BCI), and neurorobotics. His work focuses on developing intelligent, noninvasive systems that bridge the gap between neural activity and robotic control. In his highly cited 2017 paper, Shahabi introduced a novel brain-robot interface that decodes motor imagery EEG signals to control a two-degree-of-freedom robot. By optimizing a Support Vector Machine (SVM) classifier with Particle Swarm Optimization (PSO), he achieved robust, real-time control—a significant step toward practical BCI-driven prosthetics and assistive devices. His 2018 research further advances the field by integrating spiking neural networks with Spike Timing-Dependent Plasticity (STDP) to enable a robotic arm to learn target-reaching tasks, mimicking biological learning processes. This work models six distinct brain areas, demonstrating how sensory feedback can drive adaptive motor control. Though early in his career, Shahabi’s contributions are already garnering attention (with papers cited 7 and 3 times respectively), establishing him as an innovator in bio-inspired robotics and intelligent signal processing. His research holds promise for next-generation neural interfaces and autonomous learning systems.