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.
Existing studies have shown functional brain networks in patients with major depressive disorder (MDD) have abnormal network topology structure. But the methods to construct brain network still exist some issues to be solved.
Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the 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.