Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network

Table 6

CNN network accuracy in image classification after 10 minutes for SAETM features and ten other features.

Time (m)	Features
	Power theta	Power alpha	Power beta	Power gamma	Mean	Standard deviation	Zero-crossingrate	Fractal dimension	Approximate entropy	Correlation dimension	SAETM
1	0.4281	0.4323	0.4303	0.4183	0.4065	0.3619	0.4003	0.4549	0.4426	0.4273	0.4874
2	0.4170	0.4382	0.4580	0.4089	0.4103	0.3547	0.4097	0.4609	0.4432	0.4175	0.4787
3	0.4295	0.4693	0.4531	0.4198	0.4074	0.3683	0.4162	0.4656	0.4594	0.4145	0.5996
4	0.4536	0.4726	0.4854	0.4186	0.4238	0.3605	0.4229	0.4768	0.4529	0.4291	0.6491
5	0.4609	0.4812	0.4930	0.4347	0.4239	0.4038	0.4140	0.4611	0.4535	0.4043	0.7752
6	0.4729	0.4983	0.5034	0.4279	0.4158	0.3794	0.4371	0.4662	0.4485	0.4296	0.7923
7	0.4582	0.4978	0.5012	0.4376	0.4283	0.3775	0.4395	0.4693	0.4526	0.4352	0.8164
8	0.4901	0.5391	0.5078	0.4594	0.4192	0.3738	0.4588	0.4738	0.4594	0.4808	0.8283
9	0.5865	0.6449	0.5539	0.5523	0.4763	0.3875	0.4521	0.5532	0.4820	0.4986	0.8298
10	0.6534	0.6710	0.5730	0.5915	0.4872	0.3916	0.4716	0.5610	0.5201	0.5072	0.8305