Shiraishi Yasushi, Katayama Norihiro, Takahashi Tetsuya, Karashima Akihiro, Nakao Mitsuyuki
Biomodeling Laboratory, Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4019-22. doi: 10.1109/IEMBS.2009.5333505.
Multiunit recording with multi-site electrodes in the brain has been widely used in neuroscience studies. After the data recording, neuronal spikes should be sorted according to the pattern of spike waveforms. For the spike sorting, independent component analysis (ICA) has recently been used because ICA has potential for resolving the problem to separate the overlapped multiple neuronal spikes. However the performance of spike sorting by using ICA has not been examined in detail. In this study, we quantitatively evaluate the performance of ICA-based spike sorting method by using simulated multiunit signals. The simulated multiunit signal is constructed by compositing real extracellular action potentials recorded from guinea-pig brain. It is found that the spike sorting by using ICA hardly avoids significant false positive and negative errors due to the cross-talk noise contamination on the separated signals. The cross-talk occurs when the multiunit signal of each recording channel have significant time difference; this situation does not satisfy the assumption of instantaneous source mixture for the major ICA algorithms. Since the channel delay problem is hardly resolved, an ICA algorithm which does not require the instantaneous source mixing assumption would be appropriate for use of spike sorting.
在大脑中使用多位点电极进行多单元记录已在神经科学研究中广泛应用。数据记录后，神经元尖峰应根据尖峰波形模式进行分类。对于尖峰分类，最近使用了独立成分分析（ICA），因为ICA有潜力解决分离重叠的多个神经元尖峰的问题。然而，使用ICA进行尖峰分类的性能尚未得到详细研究。在本研究中，我们通过使用模拟多单元信号定量评估基于ICA的尖峰分类方法的性能。模拟多单元信号是通过合成从豚鼠大脑记录的真实细胞外动作电位构建的。研究发现，由于分离信号上的串扰噪声污染，使用ICA进行尖峰分类几乎无法避免显著的假阳性和假阴性错误。当每个记录通道的多单元信号存在显著时间差时会发生串扰；这种情况不满足主要ICA算法的瞬时源混合假设。由于通道延迟问题几乎无法解决，一种不需要瞬时源混合假设的ICA算法将适合用于尖峰分类。
