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About

Abdullah Akce’s research sits at the intersection of brain-machine interfaces, robotics, and human motion modeling, with a focus on enabling humans to control mobile robots using only electroencephalograph (EEG) signals. His most-cited work, “A Brain–Machine Interface to Navigate a Mobile Robot in a Planar Workspace: Enabling Humans to Fly Simulated Aircraft With EEG” (2012, 26 citations), pioneered a method for translating noisy, low-bit-rate EEG inputs into symbolic commands for robot navigation, allowing users to guide a robot along fixed-speed paths in a planar workspace. Expanding on this, his 2012 paper (4 citations) introduced a cost-function-based approach to generate human-like, obstacle-aware trajectories from EEG signals, while his 2011 work (2 citations) developed a compact representation of locally-shortest paths for human-robot interfaces. Akce also contributed to the theoretical foundations of human locomotion, offering critical commentary (2010, 2 citations) on optimality principles governing walking paths. Though his citation counts are modest, his work is notable for tackling the challenging problem of real-time, low-bandwidth neural control, bridging computational geometry and neuroengineering. His achievements highlight a creative approach to making robot control accessible through brain signals, with implications for assistive technologies and human-robot collaboration.