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Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration...
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...
The fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls. Traditional control schemes are only capable of providing 2 degrees of freedom, which is insufficient for dexterous control of individual fingers. We present a framework where myoelectric signals from natural hand and finger movements can be decoded with a high accuracy. 32 surface-EMG electrodes...
Advances in brain-computer interfaces (BCI) have enabled direct neural control of robotic and prosthetic devices. However, it remains unknown whether cortical signals can be decoded in real-time to replicate dexterous movements of individual fingers and the wrist. In this study, single unit activity from 115 task-related neurons in the primary motor cortex (Ml) of a trained rhesus monkey were recorded,...
A novel scheme for real time detection of ischemic features from long term electrocardiograms (ECG), based on the dilated discrete Hermite expansion is proposed. The discrete Hermite functions used for the expansion are eigenvectors of a symmetric tridiagonal matrix that commutes with the centered Fourier matrix. The ECG signals were expanded in terms of Hermite functions using a simple dot product...
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