A Framework for Hardware-Software Co-Design for Real Time and Automatic Spike Sorting of Multichannel Neuronal Activity

• Main Authors: Lin, Kuan-Fu, 林冠甫
• Other Authors: Huang, Sheng-Chieh
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/47317710648413240500

碩士 === 國立交通大學 === 電控工程研究所 === 100 === Spike sorting is a primary and essential procedure for the realization of brain in the neuroscience and it provides a connection between the neural behavior and external behavior of animal for further application such as movement prediction and brain machine interface (BMI). With different objectives and improvement in implantable device technology, multichannel recording has become a standard tool for the research on neurophysiology. Besides, the accuracy of the spike sorting has crucial relations with the stability of the advanced application, and it would result in fatal influence for the application related to humans if the spike sorting was not reliable. A real-time and automatic spike sorting system for 16-channel neural recording based on hardware-software co-design is proposed in this study. The two-stage spike detection, combining the benefit of threshold method and nonlinear energy operator (NEO), is presented as the initial step of spike sorting process. The feature extraction in this study utilizes the discrete derivative method to improve the spike separation and chooses the principal component analysis to select few dominant features for reduction of indistinctive data. The single linkage method, with Mahalanobis distance as the distance metric, is used for spike clustering. The cross electrode validation is presented for the purpose that validates whether there is a single neuron recorded by two or more electrodes. The algorithms for this multichannel spike sorting system were verified and evaluated through simulations and experiments. The two-stage spike detection cooperating with feedback rule could decrease the probability of false detection. There is a significant improvement for spike separation on feature space with the help of the discrete derivative method, and, thus, the accuracy of the spike sorting is enhanced on indistinguishable data set from the result. After spike sorting, the cross electrode validation could lower the redundant neuronal information to be recorded. The proposed framework for real-time and automatic spike sorting of multichannel neuronal activity is feasible as the first step for neuroscientist to figure out the brain function of animals.