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

Table 4

Classification accuracy of all the tested methods.

	-means	FCM		DBC			O-sort
						automatic merging
PCA	97.16 (4.09)	98.46 (9.64)	97.11 (6.45)	94.76 (12.15)	91.04 (27.45)	97.66 (5.92)	—
DWT	96.42 (4.78)	77.18 (31.08)	95.29 (8.81)	93.60 (12.68)	76.13 (24.47)	93.90 (13.39)	—
FSDE	68.42 (29.64)	69.83 (14.07)	63.66 (33.26)	67.01 (25.22)	36.97 (50.62)	56.71 (21.24)	—
GEO	79.57 (17.82)	72.25 (28.26)	77.43 (28.63)	82.35 (19.77)	84.99 (18.29)	88.10 (20.04)	—
	—	—	—	—	—	—	94.37 (4.75)

Spike sorting classification accuracy, CA (%), on the simulated data sets for all the possible combinations of FE (rows) and clustering algorithms (columns) and for O-sort. CA is presented as median and (IQR) over the different signals (). For each FE method (i.e., first 4 rows of the table) the asterisk points out the clustering method with the statistically highest CA performance (, the Friedman + Wilcoxon test). Accuracies lower than 70% (e.g., FSDE method) were not considered acceptable.