A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis

Table 2

Properties of the selected feature extraction methods.

	Domain	Percentage of publications; (with respect to Table 1)	Need of training before on-line FE
Principal Components Analysis (PCA)	Time	36%	Yes
First and Second Derivative Extrema (FSDE)	Time	3%	No
Geometric features (GEO)	Time	13%	Yes
Discrete Wavelet Transform (DWT)	Time/scale	26%	Yes
Other methods	—	22%	—

The “domain” column refers to the analysis domain in which each method works, for example, time domain or time/scale domain. The “percentage of publications (with respect to Table 1)” is the ratio between the number of publications dealing with a certain FE method and the total number of analyzed publications (reported in Table 1). To esteem this parameter, works dealing with methods were counted as different works in the denominator. Different works of the same authors using the same FE method were counted as 1. The “need of training before on-line FE” column indicates whether a preliminary training phase on a first set of acquired spikes is needed in order to run the method in on-line mode.