Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme

Table 2

Analysis of proposed DS-EFO-DCN ocular artifacts detection and mitigation model over existing meta heuristic algorithms in terms of RMSE for (A) Subject 1, (B) Subject 2, (C) Subject 3, (D) Subject 4, (E) Subject 5, (F) Subject 6, (G) Subject 7, (H) Subject 8, and (I) Subject 9.

Subject No.	SNR	RMSE
		PSO-DCN [8]	GWO-DCN [7]	DPE-EWA-DCN [38]	EFO-DCN [11]	DS-EFO-DCN
Subject 1	0.5	6.1	7.0	7.8	9.1	8.8
	0.7	5.5	6.9	6.8	6.7	6.5
	1.0	4.1	6.3	5.5	5.0	4.5
	1.3	3.8	5.5	5.2	5.0	4.0
	1.5	3.6	5.1	5.0	5.1	3.2
Subject 2	0.5	4.9	5.0	5.8	6.5	6.5
	0.7	4.4	4.3	4.7	4.5	4.5
	1.0	3.8	3.9	3.4	3.4	3.3
	1.3	3.5	3.7	2.8	2.8	2.5
	1.5	3.8	3.5	2.4	2.7	2.1
Subject 3	0.5	7.8	8.4	11.0	10.9	11.1
	0.7	7.0	7.3	8.0	8.0	8.3
	1.0	6.8	6.8	6.0	5.9	6.0
	1.3	6.5	6.2	4.8	4.7	4.6
	1.5	6.2	6.0	4.3	5.0	4.0
Subject 4	0.5	5.7	5.5	5.5	6.0	5.7
	0.7	4.4	4.4	3.8	3.6	4.4
	1.0	3.5	3.5	3.0	3.0	3.0
	1.3	2.7	3.1	2.5	2.6	2.5
	1.5	2.7	3.0	2.3	2.4	2.2
Subject 5	0.5	8.0	5.8	5.8	5.8	7.1
	0.7	5.8	4.4	4.5	4.5	5.2
	1.0	4.0	4.0	3.5	3.6	3.8
	1.3	3.7	3.8	3.2	3.3	3.2
	1.5	3.0	3.5	2.8	2.9	2.8
Subject 6	0.5	10.5	11.0	10.1	10.6	9.8
	0.7	7.3	8.8	8.0	8.0	7.7
	1.0	7.0	7.3	6.0	6.0	6.0
	1.3	6.9	6.7	4.8	5.0	5.1
	1.5	6.7	6.3	4.0	4.9	4.5
Subject 7	0.5	5.0	4.8	4.7	4.7	5.4
	0.7	4.2	4.3	3.7	3.8	4.4
	1.0	3.5	3.4	3.0	3.4	3.2
	1.3	3.1	3.1	2.7	2.7	2.4
	1.5	2.6	2.7	2.5	2.6	2.1
Subject 8	0.5	11.0	9.7	10.1	10.1	10.0
	0.7	8.7	7.0	7.3	7.3	7.2
	1.0	6.8	5.9	5.2	6.0	5.1
	1.3	5.9	5.5	4.2	4.3	4.0
	1.5	5.5	5.5	3.4	3.5	3.4
Subject 9	0.5	17.8	17.9	14.2	15.8	14.0
	0.7	12.5	13.8	13.5	11.7	10.3
	1.0	9.8	10.5	8.0	9.9	7.3
	1.3	7.9	9.8	7.7	8.0	5.8
	1.5	7.5	9.5	6.0	7.8	4.5