中文摘要：

细胞外神经元锋电位记录中经常包含许多小幅值信号，为了正确检测这些小幅值低信噪比的锋电位，增加单次实验的神经元检出数量，设计一种针对四极电极阵列记录信号的锋电位检测算法．提取4通道信号主成分分析的第一分量，计算该分量的非线性能量算子，从而减小噪声并增强锋电位．检测阈值的设定采用一种两步法，用于减小锋电位发放密度变化以及大幅值锋电位对于阈值的影响．仿真数据和实验记录数据的验证结果表明，这种主成分与非线性能量算子相结合的阈值检测法适用于四极电极等测量点高密度分布的微电极阵列记录信号，能够显著提高小幅值低信噪比锋电位信号的正确检出率，特别是能够有效地检出重叠锋电位，为后续的神经信息解码和神经网络分析提供更充分的数据．

英文摘要：

There are usually a large number of small amplitude pulses included in extracellular action potential pulse （i. e. spike） recordings. In order to accurately detect these small spikes from recording signals with low signal-to-noise ratio （SNR） and thereby to increase the number of identified neurons from a single experiment, the present work developed a new spike detection algorithm based on the features of tetrode recording signals. The method firstly extracted the first component of the four channel signals by using principal component analysis （PCA）. Then, the nonlinear energy operator （NEO） was applied on the first component to obtain the signals with low noises and enhanced spikes for spike detection by using a threshold method. The detection threshold was determined by a type of two step method to decrease the influences from varied spike firing densities and from large amplitude spikes. The results obtained from both synthetic datasets and experimental recordings demonstrate that the PCA-NEO threshold method can be used to processing signals recorded by microelectrode arrays with tetrode-like high density spacings. It is able to significantly increase the accurate detection ratio of small spikes with low SNR. Especially, the method can identify overlapped spikes effectively. Therefore, the new spike detection method can provide more information for neuronal signal decoding and neural network analysis.