Accuracy Optimization of the Spike Sorting Algorithm for Classification of Neural Signals

The Spike Sorting is an algorithm that allows extracting peculiar features from the neural signals and uniquely identifying the neurons that contributed to the generation of the recording. The literature shows that researches on this topic do not pay the due attention to the optimization process of the algorithm parameters. Here, an optimization process based on the multimodality approach is presented. It was aimed to select the best set of features to increase the accuracy of classification of neural signals. Simulated recordings were used to validate the approach. We demonstrated that triplets of optimized features were able to discriminate among 10 classes with an accuracy of ~95%; on the other hand, a fixed triplet reached an accuracy of ~90%. Moreover, accuracy decay with respect to the classes was slower and surprisingly more predictable.