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Previous research has shown that neuronal activity can be used to continuously decode the kinematics of gross movements involving arm and hand trajectory. However, decoding the kinematics of fine motor movements, such as the manipulation of individual fingers, has not been demonstrated. In this study, single unit activities were recorded from task-related neurons in M1 of two trained rhesus monkey...
In neuroprosthetic systems, decoding based on a sparse population of task-related neurons is impractical because micro-electrode arrays often drift gradually in the cortex. Since the neuronal population being recorded from is dynamic, it is favorable to have a larger number of neurons containing information relevant to movement decoding and to decrease the relative importance of individual neurons...
A brain-computer interface (BCI) uses electrophysiological measures of brain function to enable individuals to communicate with the external world, bypassing normal neuromuscular pathways. While it has been suggested that this control can be applied for neuroprostheses, few studies have demonstrated practical BCI control of a prosthetic device. In this paper, an electroencephalogram (EEG)-based motor...
Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT)...
Dynamic synchronization between different brain regions has long been considered as the underlying neural mechanism of sensory, motor and cognitive functions. Practical methods of accurately quantifying this kind of dynamics by using scalp EEG are plagued by volume conduction effects and background noise. We propose a new method of measuring transient phase locking between independent components underlying...
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