Performance Comparison of Extracellular Spike Sorting Algorithms for Single-Channel Recordings
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Proper classification of action potentials from extracellular recordings is essential for making an accurate study of neuronal behavior. Many spike sorting algorithms have been presented in the technical literature. However, no comparative analysis has hitherto been performed. In our study, three widely-used publicly-available spike sorting algorithms (WaveClus, KlustaKwik, OSort) were compared with regard to their parameter settings. The algorithms were evaluated using 112 artificial signals (publicly available online) with 2–9 different neurons and varying noise levels between 0.00 and 0.60. An optimization technique based on Adjusted Mutual Information was employed to find near-optimal parameter settings for a given artificial signal and algorithm. All three algorithms performed significantly better (p < 0.01) with optimized parameters than with the default ones. WaveClus was the most accurate spike sorting algorithm, receiving the best evaluation score for 60% of all signals. OSort operated at almost five times the speed of the other algorithms. In terms of accuracy, OSort performed significantly less well (p < 0.01) than WaveClus for signals with a noise level in the range 0.15–0.30. KlustaKwik achieved similar scores to WaveClus for signals with low noise level 0.00–0.15 and was worse otherwise. In conclusion, none of the three compared algorithms was optimal in general. The accuracy of the algorithms depended on proper choice of the algorithm parameters and also on specific properties of the examined signal.
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