Zahid Razzaq

Free University of Bozen-Bolzano, Beijing Institute of Technology

Papers

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About

Zahid Razzaq is a pioneering researcher at the intersection of brain-computer interfaces (BCI) and intelligent control systems, with a focus on restoring mobility and independence to individuals with severe neuromuscular disorders. His work centers on developing adaptive, self-learning algorithms that translate neural signals into precise commands for assistive robotics. Razzaq’s most cited paper, “Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach” (2024, 12 citations), introduces a novel neuro-fuzzy framework that enables mobile robots to learn and adapt to a user’s brainwave patterns in real time, dramatically improving responsiveness and safety. His earlier foundational study, “Fuzzy-Based Shared Control for Brain-controlled Mobile Robot” (2020, 5 citations), established a shared control paradigm that balances user intent with autonomous navigation, reducing cognitive load for disabled users. Collectively, his contributions have advanced the practical viability of EEG-driven wheelchairs and robotic aids, directly impacting patients with ALS, cerebral palsy, and spinal cord injuries. Razzaq’s work is notable for its integration of fuzzy logic with machine learning, offering a robust solution to the noise and variability inherent in neural signals. With growing citation impact, he is emerging as a key innovator in assistive neuro-robotics.