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About

Omar Farooq is a researcher whose work sits at the intersection of neural engineering, brain-computer interfaces (BCIs), and machine learning, with a particular focus on harnessing EEG signals to restore mobility and independence for individuals with neuromuscular disorders. Beginning as early as 2012, Farooq pioneered investigations into non-invasive EEG-based systems for detecting limb movements — including elbow and wrist motion — translating brain activity into commands for robotic actuators and prosthetic arms. This foundational work challenged the prevailing reliance on invasive techniques like ECoG, advocating instead for accessible, non-invasive alternatives. His 2013 and 2014 studies on wrist movement detection and robotic arm control through BMI helped establish practical frameworks for assistive technology design. More recently, Farooq has expanded into applying machine learning methodologies — comparing algorithms such as SVM, Random Forest, and Autoencoders for EEG classification — and exploring motor imagery signal decoding, reflecting the field's evolution toward data-driven approaches. His 2021 work on EEG-based exoskeletons for rehabilitation therapy further demonstrates his commitment to clinical translation. With a growing citation record across a decade of contributions, Farooq represents a steady and dedicated voice in accessible neurotechnology research.