Table 2.

Number of samples N, after removing corrupted signals, corresponding to each electrode, and headset analyzed.

	BPG	BPD	M32	M4S	SS	Total (N)	Kernel	σ	k dominant eigenvalues	k selected
Fp1	3,085	845	1,327	460	—	5,717	Gauss	26	5	5
Fp2	3,085	845	1,349	460	—	5,739	Gauss	26	5	5
F3	3,085	832	1,349	—	1,415	6,681	Gauss	22	4	5
F4	3,085	841	1,349	—	1,404	6,679	Gauss	20	4	5
C3	3,085	845	1,349	—	1,415	6,694	Gauss	20	5	5
C4	3,085	845	1,343	—	1,380	6,653	Gauss	20	5	5
P3	3,085	845	1,349	—	1,415	6,694	Gauss	26	4	5
P4	3,085	845	1,349	—	1,372	6,651	Gauss	20	5	5
Total number of PSD segments	3,085	845	1,349	460	1,415

The value σ in the Gaussian Kernel corresponds to the best performing σ for each case. All clusters were performed by selecting k = 5 cluster centers to find.

“—” Indicates the headset did not contain that electrode. Gaussian kernel: $K(x,x^{\prime })=exp(-\frac{\left|\right|x-x^{\prime }|{|}^{\mathit{\text{2}}}}{\mathit{\text{2}}{\sigma }^{\mathit{\text{2}}}})$, where ||x−x′||² is the squared Euclidean distance between the two feature vectors, σ is a scaling parameter.