Papers
10
Total Citations
151
H-Index
8
About
Mojisola Grace Asogbon is a leading researcher in neural engineering and rehabilitation robotics, specializing in decoding motor intent from electromyogram (EMG) and electroencephalography (EEG) signals. Her work addresses critical challenges in myoelectric control systems, particularly for multifunctional prostheses and stroke rehabilitation robots. Asogbon pioneered methods to resolve the co-existing impacts of multiple dynamic factors on EMG-pattern recognition performance—her most cited work (52 citations) that has shaped robust prosthetic control design. She developed spatiotemporal and adaptive filtering approaches for decoding movement intent in active motor training systems (23 citations), and introduced novel feature extraction frameworks like the spatio-temporal based descriptor for limb movement characterization (15 citations). Her contributions extend to signal reconstruction with the GBRAMP algorithm (13 citations) and attention-driven deep neural networks for continuous knee joint kinematics estimation during running (11 citations). Asogbon has also advanced motor imagery decoding for IoT-integrated brain-computer interfaces and multi-artifact removal techniques for upper extremity EEG analysis. With over 150 total citations across her publications, her research directly impacts the development of intuitive, user-responsive rehabilitation robots and prosthetic systems that restore function and independence to individuals with motor impairments.
Research Focus
Key Achievements
Top Papers
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