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.
High-definition transcranial direct current stimulation (HD-tDCS) is a potential neuromodulation apparatus for stroke rehabilitation. However, its modulatory effects in stroke subjects is still not well understood.
Osseointegrated transradial prostheses have the potential to preserve the natural range of wrist rotation, which improves the performance of activities of daily living and reduces compensatory movements that potentially lead to secondary health problems over time.
Simultaneous and proportional control (SPC) of neural-machine interfaces uses magnitudes of smoothed electromyograms (EMG) as control inputs. Though surface EMG (sEMG) electrodes are common for clinical neural-machine interfaces, intramuscular EMG (iEMG) electrodes may be indicated in some circumstances (e.g., for controlling many degrees of freedom).
This paper aimed to develop a novel electromyography (EMG)-based neural-machine interface (NMI) that is user-generic for continuously predicting coordinated motion between metacarpophalangeal (MCP) and wrist flexion/extension.
Developing an artificial arm with functions equivalent to those of the human arm is one of the challenging goals of bioengineering. State-of-the-art prostheses lack several degrees of freedom and force the individuals to compensate for them by means of compensatory movements, which often result in residual limb pain and overuse syndromes.