The Relationship Between the Neural Signal of Primary Somatosensory Cortex and Tactile Stimuli of Hindlimb in Rats

碩士 === 國立臺北科技大學 === 電腦與通訊研究所 === 96 === Brain neural signal is an important message of studying the brain function. Previous studying found the different cortex regions mapped the different function. Therefore, recording and analyzing the neural signal in the somatosensory cortex will help to unders...

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Bibliographic Details
Main Authors: Wen-Liang Tsai, 蔡文亮
Other Authors: 駱榮欽
Format: Others
Language:en_US
Published: 2008
Online Access:http://ndltd.ncl.edu.tw/handle/26xz54
Description
Summary:碩士 === 國立臺北科技大學 === 電腦與通訊研究所 === 96 === Brain neural signal is an important message of studying the brain function. Previous studying found the different cortex regions mapped the different function. Therefore, recording and analyzing the neural signal in the somatosensory cortex will help to understand the mechanism of sensory function. The goal of this research is to represent the relationship between the neural signal of primary somatosensory cortex (S1) and tactile stimuli in rats by using a homemade system. The system includes four parts: neural signal acquisition, amplifying, recording, and analysis. In the acquisition part, we use multichannel electrodes of microwire to pick up the neural signal of brain cortex of rats. The amplifying part is used to filter and boost signal. The recording part is used to store the amplified data into computer for further processing. As for the analysis part, we use some digital signal processing and statistics techniques to extract the underlying information hidden in the neural signal. In the study, we use the different sharp stimulant to stimulate the hindlimb of rat, and recording the response signals from S1HL. Then analyze the relationships between these neural signals and the stimuli. The analytic processes are to utilize the nonlinear energy operator (NLEO) to detect exactly the response time of evoked potential by stimulation of hindlimb in rat. Then signals are separated into each section according to the response time. Further, we combine independent component analysis (ICA) and dynamic dimension increasing algorithm (DDIA) to extract features of signals of all sections. Lastly these features are classified by K-means. The results have shown the accuracy above 80% in distinguishing the hindlimb is stimulated or not. then to distinguish from different stimulants is 40% to 70%. Moreover, the properties of neural signals of S1HL on cortex of both hemispheres are similar. These results indicate that the techniques develop in this study would benefit researchers in neuroscience studies.