Muhammad Ahmed Khan

About

Muhammad Ahmed Khan is a leading researcher in neurorehabilitation engineering, with a primary focus on developing brain-computer interface (BCI) systems for post-stroke motor recovery. His work centers on motor imagery (MI) based BCI technologies, which translate neural signals into commands for robotic assistive devices, offering new hope to stroke survivors suffering from motor impairments. Khan’s most influential contribution is his comprehensive 2020 review on MI-based BCI systems for upper limb neurorehabilitation, which has garnered 249 citations and serves as a foundational resource for the field. He has also advanced the integration of flexible technologies and hybrid EEG-EMG systems, enabling real-time control of robotic arms for amputees and disabled individuals. His 2021 review on flexible technology in post-stroke rehabilitation (34 citations) and his 2022 work on MI EEG signal classification (15 citations) further demonstrate his commitment to practical, patient-centered solutions. By bridging signal processing, robotics, and clinical application, Khan is shaping the future of accessible, intelligent rehabilitation technologies.

Research Focus

Key Achievements

Top Papers

1. 1
   Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application

   249 citations · 2020
2. 2
   Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review

   34 citations · 2021
3. 3
   Motor Imagery EEG Signal Classification for Stroke Survivors Rehabilitation

   15 citations · 2022
4. 4
   Hybrid EEG-EMG based brain computer interface (BCI) system for real-time robotic arm control

   12 citations · 2019

Key Collaborators

Contact & Links