Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach

Table 1

Performances of subjects of the age group 20–28 using the AR Yule-Walker features with FFNNCSA.

Subjects	Ten trials’ average training time (sec)	Ten trials’ average testing time (sec)	Average classification accuracy (%)
			Sd	Max	Min	Mean
S1	18.79	0.76	1.87	96.82	89.95	94.78
S2	18.62	0.78	1.73	96.67	89.50	93.54
S3	18.30	0.72	1.38	98.34	90.72	95.42
S4	18.32	0.77	1.53	96.23	90.16	94.10
S5	18.95	0.73	1.68	96.38	89.66	94.38
S6	18.68	0.75	1.57	96.74	89.82	94.66
S7	18.44	0.76	1.62	96.80	90.00	94.68
S8	18.56	0.74	1.70	95.89	89.58	94.40
S9	18.78	0.72	1.71	95.92	89.76	94.84
S10	18.18	0.71	1.34	98.76	90.85	95.78