M. G. Asogbon

发表

Determining the Optimal Window Parameters for Accurate and Reliable Decoding of Multiple Classes of Upper Limb Motor Imagery Tasks

Oluwarotimi Williams Samuel, Guanglin Li, Yanjuan Geng, 2018, 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS).

Efficient Classification of Motor Imagery using Particle Swarm Optimization-based Neural Network for IoT Applications

Oluwarotimi Williams Samuel, Guanglin Li, Mojisola Grace Asogbon, 2020, 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT.

Identifying oscillations under multi-site sensory stimulation for high-level peripheral nerve injured patients: a pilot study

Guanglin Li, Linling Li, M. G. Asogbon, 2022, Journal of neural engineering.

Contrast of multi-resolution analysis approach to transhumeral phantom motion decoding

Guanglin Li, M. G. Asogbon, E. Nsugbe, 2021, CAAI Trans. Intell. Technol..

Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

Oluwarotimi Williams Samuel, Guanglin Li, Mojisola Grace Asogbon, 2021, IET Cyber-Systems and Robotics.

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

Oluwarotimi Williams Samuel, Guanglin Li, Arun Kumar Sangaiah, 2017, Expert Syst. Appl..

A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks

Ganesh R. Naik, Oluwarotimi Williams Samuel, Yanjuan Geng, 2020, Future Gener. Comput. Syst..

Decoding movement intent patterns based on spatiotemporal and adaptive filtering method towards active motor training in stroke rehabilitation systems

Oluwarotimi Williams Samuel, Guanglin Li, Yanjuan Geng, 2021, Neural Computing and Applications.

A Channel Selection Approach Based on Convolutional Neural Network for Multi-channel EEG Motor Imagery Decoding

Oluwarotimi Williams Samuel, Mojisola Grace Asogbon, Chee Siang Ang, 2019, 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE).

A study on preterm birth predictions using physiological signals, medical health record information and low‐dimensional embedding methods

Oluwarotimi William Samuel, Guanglin Li, M. G. Asogbon, 2021, IET Cyber-Systems and Robotics.

Intelligent EMG Pattern Recognition Control Method for Upper-Limb Multifunctional Prostheses: Advances, Current Challenges, and Future Prospects

Oluwarotimi Williams Samuel, Guanglin Li, Yanjuan Geng, 2019, IEEE Access.

Resolving the adverse impact of mobility on myoelectric pattern recognition in upper-limb multifunctional prostheses

Oluwarotimi Williams Samuel, Guanglin Li, Yanjuan Geng, 2017, Comput. Biol. Medicine.

Fuzzy Driven Decision Support System for Enhanced Employee Performance Appraisal

Oluwarotimi Williams Samuel, Bolanle Adefowoke Ojokoh, Mojisola Grace Asogbon, 2020, Int. J. Hum. Cap. Inf. Technol. Prof..

Enhanced Neuro-Fuzzy System Based on Genetic Algorithm for Medical Diagnosis

Oluwarotimi Williams Samuel, Mojisola Grace Asogbon, M. O. Omisore, 2016 .

Towards optimal selection of stimuli polarity method for effective evoking auditory brainstem responses.

Guanglin Li, Shixiong Chen, M. G. Asogbon, 2021, Journal of integrative neuroscience.

GBRAMP: A generalized backtracking regularized adaptive matching pursuit algorithm for signal reconstruction

Oluwarotimi Williams Samuel, Guanglin Li, Mojisola Grace Asogbon, 2021, Comput. Electr. Eng..

A Novel Time-Domain Descriptor for Improved Prediction of Upper Limb Movement Intent in EMG-PR System

Lin Wang, Oluwarotimi Williams Samuel, Guanglin Li, 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Ascertaining the optimal myoelectric signal recording duration for pattern recognition based prostheses control

Guanglin Li, Shixiong Chen, Frank Kulwa, 2023, Frontiers in Neuroscience.

A linearly extendible multi-artifact removal approach for improved upper extremity EEG-based motor imagery decoding.

Guanglin Li, E. Scheme, Xiangxin Li, 2021, Journal of neural engineering.

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

Lin Wang, Oluwarotimi Williams Samuel, Arun Kumar Sangaiah, 2020, Symmetry.

Towards resolving the co-existing impacts of multiple dynamic factors on the performance of EMG-pattern recognition based prostheses

Oluwarotimi Williams Samuel, Guanglin Li, Yanjuan Geng, 2019, Comput. Methods Programs Biomed..

Realizing Efficient EMG-Based Prosthetic Control Strategy.

Oluwarotimi Williams Samuel, Guanglin Li, Mojisola Grace Asogbon, 2019, Advances in experimental medicine and biology.

An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running

Guanglin Li, K. Nazarpour, M. G. Asogbon, 2023, Biomedical Signal Processing and Control.

A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

Guanglin Li, Frank Kulwa, M. G. Asogbon, 2023, 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

High-density surface EMG signal quality enhancement via optimized filtering technique for amputees' motion intent characterization towards intuitive prostheses control

Yazan Ali Jarrah, Guanglin Li, Xin Wang, 2022, Biomed. Signal Process. Control..

A Robust Multi-Channel EEG Signals Preprocessing Method for Enhanced Upper Extremity Motor Imagery Decoding

Oluwarotimi Williams Samuel, Guanglin Li, Yanjuan Geng, 2020, 2020 IEEE International Conference on Mechatronics and Automation (ICMA).

Multi-technique object tracking approach- A reinforcement paradigm

Oluwarotimi Williams Samuel, Guanglin Li, Arun Kumar Sangaiah, 2017, Comput. Electr. Eng..

A Pilot on the use of Stride Cadence for the Characterization of Walking Ability in Lower Limb Amputees

J. J. Reyes-Lagos, M. G. Asogbon, E. Nsugbe, 2024, Biomedical Engineering Advances.