Automatic onlinespike sorting with singular value decomposition and fuzzy C-mean clustering

BMC Neuroscience

Table 1 Number of classification errors and noise levels, obtained using FSPS, SPC and K-means, in all simulated examples

№	Example no.	Noise level	Number of noisy spikes	Classification errors
				SPC			K-means		FSPS
				Spike Shape	PCA	Wavelets	PCA	Wavelets	PSVD
		1	2	3	4	5	6	7	8
1.	1	[0.05]	2729	0	1	1	0	0	0
2.		[0.10]	2753	0	17	5	0	0	0
3.		[0.15]	2693	0	19	5	0	0	1
4.		[0.20]	2678	24	130	12	17	17	47
5.		[0.25]	2586	266	911	64	68	69	157
6.		[0.30]	2629	838	1913	276	220	177	221
7.		[0.35]	2702	1424	1926	483	515	308	354
8.		[0.40]	2645	1738	1738	741	733	930	462
9.	2	[0.05]	2619	2	4	3	0	0	0
10.		[0.10]	2694	59	704	10	53	2	2
11.		[0.15]	2648	1054	1732	45	336	31	27
12.		[0.20]	2715	2253	1791	306	740	154	48
13.	3	[0.05]	2616	3	7	0	1	0	0
14.		[0.10]	2638	794	1781	41	184	850	0
15.		[0.15]	2660	2131	1748	81	848	859	17
16.		[0.20]	2624	2449	1711	651	1170	874	22
17.	4	[0.05]	2535	24	1310	1	212	686	0
18		[0.10]	2742	970	946	8	579	271	7
19.		[0.15]	2631	1709	1716	443	746	546	51
20.		[0.20]	2716	1732	1732	1462	1004	872	195
Average			2663	874	1092	232	371	332	81

Noise level is represented in terms of its standard deviation relative to the peak amplitude of the spikes. All spike classes had a peak value of 1. The absolute number of false matching spikes is shown in the column 8 as the outcome of our algorithm corresponding to the datasets containing noisy spikes (column 2).

ISSN: 1471-2202