Umar Farooq Malik

National University of Sciences and Technology

Papers

Total Citations

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About

Umar Farooq Malik is a researcher at the forefront of Brain-Computer Interface (BCI) technology, with a primary focus on developing assistive systems for individuals with motor disabilities. His work centers on the intersection of biomedical signal processing, machine learning, and robotics, aiming to translate neural activity into actionable commands. Malik’s most cited paper, "A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm" (2023, 16 citations), introduces an efficient method for decoding motor imagery from EEG data. By leveraging logistic regression, his framework offers a streamlined approach to classifying brain signals, enhancing the practicality of BCI systems for real-world applications. This contribution is particularly significant for advancing non-invasive communication tools that empower users to interact with their environment through thought alone. Malik’s research not only demonstrates technical rigor but also holds profound implications for improving quality of life, making him a promising voice in the growing field of neurorehabilitation and human-machine interaction.