Clustering Algorithms
Sorting of neural spikes: When wavelet based methods outperform principal component analysis
Natural Computing: an international journal
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Extracellular recording of neural signals records the action potentials of neurons adjacent to the electrode as well as the noise generated by the overall neural activity around the electrode. The spike sorting process, i.e., detection of the noisy spikes in the recorded digital signal, feature extraction, and clustering of the spikes has been investigated extensively since it is a challenging problem for neuroscientists. However, the effects of digitization, including the sampling rate and number of bits, on the above three-stage process have not been investigated. This paper addresses the robustness of the spike sorting procedure to variations in the signal bandwidth, sampling rate, and the number of quantization levels (bit depth). Different signal-to-noise ratios (SNRs) are used and their effects on clustering are studied, when using principal components analysis (PCA) features. The PCA-based features are sbown to be robust to quantization bit depth variations while they are quite sensitive to the sampling rate even when it exceeds the Nyquist rate.