Research On Recognition Methods Of Neural Spike Signal

The extracellular recording of neural spike activity is a prerequisite for studying information transmission and processing in the brain. The spike recordings are usually obtained with electrodes. However, the recording in single electrode contains spikes from several neurons adjacent to the electrode and a high amount of background noise. Therefore, it is necessary to identify the neural spikes and find out the number of neurons contributing to the electrode recording before further analysis is carried out.Pattern recognition aims to classify data based on either a priori knowledge or on statistical information extracted from the patterns. In this dissertation, the pattern recognition methods of fuzzy clustering and support vector machine (SVM) are studied deeply, several effective classification methods are proposed to deal with the difficulties in spike sorting.The main contributions of this dissertation are summarized as follows:1. A robust fuzzy clustering method is proposed to reduce the influence of noise and outliers in spike sorting. The fuzzy membership degrees are adjusted according to the density values of data points. The noise and outliers with lower density values will have small influence in clustering process. Moreover, the border data between different clusters with low density values will have low memberships to any cluster, which make clusters well separated. In order to obtain the optimal number of clusters with ellipsoidal shapes, an extended fuzzy cluster-validity index is proposed. The fuzzy separation and compactness of clusters are evaluated using the weighted Mahalanobis distances between clusters in the index. The robust fuzzy clustering method is able to classify the real neural spikes with noisy data and outliers.2. In the presence of spike bursts or electrode drift, the spike waveforms generated by single neuron are varying with time, and then the spike clusters will be smeared and have non-convex shapes. An unsupervised hierarchical clustering based on fuzzy C means is proposed to resolve the problem. The initial clusters are obtained by fuzzy clustering method. Considering the interrelations among initial clusters and the complicated structures of spike clusters, the similarity small clusters are merged based on fuzzy membership degrees. The optimal cluster number is obtained by improved Dunn's index. The index adopts the weighted distances to calculate within-cluster scatter and between-cluster separation, which is suited for clusters with complicated structures.3. A template matching based on supervised classification method is proposed to decompose the overlapping spike waveforms. A new training method is designed for multi-class SVM, the overlapping spikes are identified by introducing synthesized overlapping waveforms into training sets. The detected overlapping spike waveforms are decomposed by template extraction. For evidence-theoretic neural network, the overlapping spikes are directly detected by predetermined thresholds, and then they are decomposed in classification process step by step.4. The SVM and its application in spike sorting are studied deeply. A weighted SVM is proposed based on density information, the density values of training data in feature space is used to adjust the distance from the hyperplane to them. The data with high density will be important to construct the separating hyperplane, while the noisy data with low density will have small influence. And then, the representative SVM is proposed to further improve the classification performance. Some important data are selected according to the distribution characteristic of training data, which should represent the distribution information. The separating hyperplane is constructed by maximizing the distance between the representative vectors and the hyperplane with all the training data being classified correctly. In this way, more useful information of the training data far away from the hyperplane is introduce to reduce the influence of outliers and improve the generalization ability of the learning machine. The proposed methods are applied to the simulated neural spikes and the representative SVM exhibits better classification performance.