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About

Hooman Nezamfar is a prominent researcher specializing in brain-computer interfaces (BCIs), neural signal processing, and assistive technologies for individuals with disabilities. His work sits at the intersection of neuroscience, machine learning, and human-computer interaction, with a particular focus on enabling communication and control through electroencephalography (EEG)-based systems. Among his most recognized contributions is his development of recursive Bayesian coding frameworks for BCIs, which advanced how task-relevant instructions can be inferred from physiological brain states, accumulating 29 citations. His FlashType™ system, a context-aware BCI typing interface leveraging code-modulated visual evoked potentials (c-VEPs), demonstrated innovative approaches to EEG-based communication with 22 citations. Nezamfar has also made significant strides in BCI-driven robotics, developing systems capable of reliably issuing real-time commands to tele-operated robots and wheelchairs, with related work attracting 15 citations. His early contributions, including an undergraduate-rooted SSVEP-based robot control system with 13 citations, highlight a career-long commitment to translating neural interface research into practical assistive applications. Collectively, Nezamfar's research has meaningfully advanced the accessibility and reliability of BCI technologies for both disabled and able-bodied users.