Rabia Avais Khan

National University of Sciences and Technology

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About

Rabia Avais Khan is a rising researcher at the intersection of brain-computer interfaces (BCIs), biomedical signal processing, and assistive robotics. Her work focuses on decoding neural activity to restore communication and mobility for individuals with severe motor disabilities. In her highly cited 2023 study, Khan introduced a novel framework that leverages logistic regression to classify two-class motor imagery EEG signals, achieving robust performance in translating brain activity into actionable commands. This contribution addresses a critical bottleneck in non-invasive BCI systems—accurate, real-time signal classification—and has garnered 16 citations, underscoring its relevance to the field. By integrating machine learning with robotics, Khan’s research paves the way for more intuitive, low-cost assistive technologies that empower users to interact with their environment through thought alone. Her work stands out for its practical emphasis on algorithmic simplicity and clinical applicability, offering a scalable solution for BCI-driven rehabilitation and communication aids. As a young investigator, Khan is establishing herself as a key voice in the push toward accessible neural interfaces, with her framework serving as a foundational tool for future studies in motor-imagery-based BCI systems.