Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography
Brain–machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach.
Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks
Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue.
Enhancement of Bilateral Cortical Somatosensory Evoked Potentials to Intact Forelimb Stimulation Following Thoracic Contusion Spinal Cord Injury in Rats
The adult central nervous system is capable of significant reorganization and adaptation following neurotrauma…
Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations from Extreme Learning
Classification algorithms used to predict intended movements of an amputee for upper-limb prosthesis control using EMG signals must tolerate changes in limb position and loads which affect those signals. Extreme Adaptive Sparse Representation Classification (EASRC) significantly outperforms other classification methods in untrained upper-limb positions, and the performance is achieved from less user training.