Extração de Características Utilizando Análise de Componentes Independentes para Spike Sorting.

Abstract

Independent component analysis (ICA) is a method which objective is to find a non gaussian, linear or non linear representation such that the components are statistically independent. As a representation, tries to capture the input data essential structure. One of ICA applications is feature extraction. A main digital signal processing issue is finding a satisfactory representation, whether for image, speech signal or any signal type for purposes such as compression and de-noise. ICA can be aplied in this direction to propose generative models of the phenomena to be represented. This work presents the problem of spike classification in extracellular records, denominated spike sorting. It is assumed that the waveforms of spikes depend on factors such as the morphology of the neuron and the distance from the electrode, so that different neurons will present different forms of spikes. However, since different neurons may have similar spikes, what makes classification very difficult, the problem is even worse due to background noise and variation os spikes of the same neuron. The spike sorting algorithm is usually divided into three parts: firstly, the spikes are detected, then projected into a feature space (with possible dimensionality reduction) to facilitate differentiation between the waveforms from different neurons, finally the cluster algorithm is run for identifying these characteristics so the spikes from the same neuron. Here, we propose the use of ICA in feature extraction stage, being this step critical to the spike sorting process, thus distinguishing the activity of each neuron detected, supporting the analysis of neural population activity near the electrode. The method was compared with conventional techniques such as Principal Component Analysis and Wavelets, demonstrating a significant improvement in results.