Mojgan Tavakolan

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About

Mojgan Tavakolan is a researcher whose work sits at the dynamic intersection of neural engineering, rehabilitation robotics, and human-machine interaction. Her research focuses primarily on brain-computer interfaces (BCIs) and electromyography-based control systems, with a particular emphasis on restoring functional independence for individuals with upper extremity impairments. Tavakolan's most influential contribution is her 2017 study on classifying three imaginary states of the same upper extremity using electroencephalographic signals and time-domain features, which has garnered 48 citations and represents a meaningful advance in multi-class BCI accuracy. This work, alongside her 2016 classification scheme for arm motor imagery (16 citations), demonstrates her sustained commitment to developing more precise and responsive control frameworks for assistive robotic devices. Together, these studies push the field toward practical, real-world applications that could meaningfully reduce disability for motor-impaired individuals. Her earlier work on wrist torque estimation using surface electromyography signals highlights her broader interest in building robust biomechanical models to improve how robotic exoskeletons respond to their wearers. Across her body of research, Tavakolan consistently bridges neuroscience and engineering, positioning her as a thoughtful contributor to the growing field of rehabilitation technology.