Mubasher Saleem

National University of Sciences and Technology

Papers

Total Citations

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About

Dr. Mubasher Saleem is a rising researcher at the intersection of biomedical engineering and artificial intelligence, with a primary focus on Brain-Computer Interfaces (BCIs) for assistive technology. His most cited work introduces a novel framework for classifying two-class motor imagery EEG signals, leveraging a logistic regression classification algorithm to enhance the accuracy and reliability of BCI systems. This contribution directly addresses the challenge of translating neural activity into actionable commands for individuals with motor disabilities, offering a computationally efficient solution that outperforms traditional methods. With 16 citations since 2023, this paper has quickly gained traction in the BCI community for its practical, low-latency approach. Dr. Saleem’s research underscores the potential of robotics and AI to decode human intent from brain signals, paving the way for more intuitive prosthetics and communication aids. His work exemplifies how machine learning can bridge the gap between neural control and real-world assistive devices, making him a promising voice in the ongoing effort to restore autonomy to those with severe motor impairments.