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In this work, we took the analysis of neural interaction based on the data recorded from the motor cortex of a monkey, when it was trained to complete multi-targets reach-to-grasp tasks. As a recently proved effective tool, Dynamic Bayesian Network (DBN) was applied to model and infer interactions of dependence between neurons. In the results, the gained networks of neural interactions, which correspond...
In this work, we recorded the spike train data from a rhesus monkey's motor cortex while it was trained to perform reach-to-grasp task at different directions and orientations. The neurons were divided into several classes according to their neuronal activities, and each class of neuron encoded a kind of functional parameter, such as direction-related, orientation-related and so on. Then we applied...
In our previous work, a non-stereotypical brain machine interface system was implemented with freely-moving rats, and a nonlinear support vector machine (SVM) classifier was used to map neural signals in the rats' motor cortices onto a set of discrete classes of directions (left and right). In this paper, we provide a comprehensive analysis about the selection of neurons and temporal parameters, which...
We have developed a method for training animals to control artificial devices from cortical signals. In this report we describe a series of experiments designed to parameterize a cortical control algorithm without an animal having to move its arm. Instead, a highly motivated animal observes as the computer drives a cursor move towards a set of targets once each in a center-out task. From the neuronal...
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