Improvement of spike train decoder under spike detection and classification errors using support vector machine
The successful decoding of kinematic variables from spike trains of motor cortical neurons is essential for cortical neural prosthesis. Spike trains from each single unit must be extracted from extracellular neural signals and, thus, spike detection and sorting procedure is indispensable but the detection and sorting may involve considerable error. Thus, a decoding algorithm should be robust with respect to spike train errors. Here, we show that spike train decoding algorithms employing nonlinear mapping, especially a support vector machine (SVM), may be more advantageous contrary to previous results which showed that an optimal linear filter is sufficient. The advantage became more conspicuous in the case of erroneous spike trains. Using the SVM, satisfactory training of the decoder could be achieved much more easily, compared to the case of using a multilayer perceptron, which has been employed in previous studies. Tests were performed on simulated spike trains from primary motor cortical neurons with a realistic distribution of preferred direction. The results suggest the possibility that a neuroprosthetic device with a low-quality spike sorting preprocessor can be achieved by adopting a spike train decoder that is robust to spike sorting errors.
spike train decoding support vector machine nonlinear mapping brainmachine interface