Automatic channel selection and neural signal estimation across channels of neural probes

High-resolution microprobes are used to record single neuron activity in the brain. This technology is envisaged to be a central component for brain-controlled computers and robots. Current neural probes, however, allow for recording only a small number of the densely spaced electrodes simultaneously. Therefore, we address the problem of autonomously choosing, for a given number, the subset of electrodes with the corresponding size so as to extract as much information as possible. We first present an approach for predicting neural spikes across different channels of the probe. Our method employs nonparametric sparse Gaussian process regression to predict the signal of a channel given the signals recorded at neighboring sites. Second, we utilize the signal predictions for efficiently seeking for the subset of electrodes that minimizes the overall prediction error. In experiments carried out using real neural data, we demonstrate that our selection procedure provides highly accurate results. Furthermore, the solutions found in our experiments are close to the optimal solution.