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| Spike Sorting Using Gaussian Mixture Model of Waveform Feature |
| Wan Hong*, Zhang Chao, Liu Xinyu, Shang Zhigang |
| (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China) |
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Abstract Spike sorting is one of basic steps to study brain information processing mechanism. Regarding to the complexity and non-stationary of spike signals, a new spike sorting method based on waveform changing rate features and Gaussian mixture model (GMM) cluster was proposed in this work, in which the GMM described the probability density function of spike features from statistical clustering viewpoint. In the proposed method, firstly, the changing rate of spike waveforms was calculated, then low-dimensional features were obtained using maximum-difference method, and the features were finally sorted by the GMM. The accuracy and feasibility of the algorithm was measured by the simulated data from the published database, and the practicality was validated by the real data from primary visual cortex of five rats and one macaque monkey. And its performance was compared to other three sorting methods that based on principal component features and GMM cluster, based on waveform features and GMM cluster as well as based on waveform changing rate features and k-mean cluster. For the simulated data, the misclassification rate of proposed method below the other three methods, that is 1.18%±1.18%, 1.41%±1.06%, 2.27%±1.51% and 2.98%±2.06% when the noise level is 0.05, 0.10, 0.15 and 0.20, respectively. For the real data, the J3 value of proposed method is 13.50±5.26 with monkey data and 13.50±5.26 with rat data. Compared with the other three methods, the proposed method gives the maximum J3 value and is higher significantly than the sorting method based on waveform features and GMM cluster. It has higher precision and classification performance as well as provides an effective approach to achieve reliable spike sorting.
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Received: 28 October 2014
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