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Abstact Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey’s arm or hand in many studies. However,...
Brain machine interfaces (BMIs), offer a direct path for brain to communicate with outside world, mainly use central neural activities to control artificial external devices. These techniques to collect the brain signals could be distinguished as non-invasive or invasive BMIs according to the position of recoding electrodes. Compared with non-invasive BMIs, invasive BMIs have wide potential in assisting,...
Probabilistic neural network (PNN), a kind of radial basis networks, is usually used for classification problems. It has the advantages of much faster training process and more accurate results using the minimum Bayesian risk criterion compared with other neural networks. In this paper, we use this neural network in brain-machine interface for decoding neural ensemble activity. Rats were trained to...
In this paper, rats were trained to press a lever over a threshold to get water as rewards, and neural ensemble activities in primary motor cortex (MI) and pressure signal of the lever were recorded synchronously. Meanwhile, two algorithms, Kalman filter (KF) and Optimal Linear Estimation (OLE), were used to decode neural ensemble activities around the pressing events. After training, the pressure...
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