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Decoding motor commands from noninvasively measured neural signals has become important in brain–computer interface (BCI) research. Applications of BCI include neurorehabilitation after stroke and control of limb prostheses. Until now, most studies have tested simple movement trajectories in two dimensions by using constant velocity profiles. However, most real-world scenarios require much more complex...
Brain-Computer Interface (BCI) systems allow users to control external devices using their thoughts. In particular, brain signals can be used to decode the trajectory of hand movements for neurorehabilitation or control of arm prostheses. Previous studies have decoded hand movement velocity during simple tasks. However, under real world conditions, patients need to control artificial limbs with more...
A brain-computer interface (BCI) can be used to control a limb neuroprosthesis in patients. In particular, decoding trajectory of upper limb with motor imagery (MI) can support motor rehabilitation using a wearable robotic arm. Recent research shows the possibility of decoding hand movement trajectory from electroencephalography (EEG) signals. However, such studies are insufficient to apply motor...
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