Table A.3.

Structure of the Deep Net classifier trial dimension = (64 ∗ 1536); kernel size = (1, floor ((5 ∗ trial dimension [1])/(256 ∗ 1))) = (1,30); pool size = (1, max (floor ((trial dimension [1] ∗ 2)/(256 ∗ 4)), 2)) = (1, 3); strides= (1, max (floor ((trial dimension [1] ∗ 2)/(256 ∗ 4)), 2)) = (1, 3).

Layer Type	Filters	Kernel size	Pool size	Strides	Dropout rate	Output
Input	-	-	-	-	-	(None, 1, 64, 1536)
Conv	25	(1, 30)	-	-	-	(None, 25, 64, 1507)
Conv	25	(1, 30)	-	-	-	(None, 25, 1, 1507)
Batch Normalization	-	-	-	-	-	(None, 25, 1, 1507)
Activation (SELU)	-	-	-	-	-	(None, 25, 1, 1507)
Average pooling	-	-	(1, 3)	(1, 3)	-	(None, 25, 1, 502)
Dropout	-	-	-	-	0.4	(None, 25, 1, 502)
Conv	50	(1, 30)	-	-	-	(None, 50, 1, 473)
Batch Normalization	-	-	-	-	-	(None, 50, 1, 473)
Activation (SELU)	-	-	-	-	-	(None, 50, 1, 473)
Average pooling	-	-	(1, 3)	(1, 3)	-	(None, 50, 1, 157)
Dropout	-	-	-	-	0.4	(None, 50, 1, 157)
Conv	100	(1, 30)	-	-	-	(None, 100, 1, 128)
Batch Normalization	-	-	-	-	-	(None, 100, 1, 128)
Activation (SELU)	-	-	-	-	-	(None, 100, 1, 128)
Average pooling	-	-	(1, 3)	(1, 3)	-	(None, 100, 1, 42)
Dropout	-	-	-	-	0.4	(None, 100, 1, 42)
Conv	200	(1, 30)	-	-	-	(None, 200, 1, 13)
Batch Normalization	-	-	-	-	-	(None, 200, 1, 13)
Activation (SELU)	-	-	-	-	-	(None, 200, 1, 13)
Max pooling	-	-	(1, 3)	(1, 3)	-	(None, 200, 1, 4)
Dropout	-	-	-	-	0.4	(None, 200, 1, 4)
Flatten	-	-	-	-	-	(None, 800)
Dense	6	-	-	-	-	(None, 2)
Activation (Softmax)	-	-	-	-	-	(None, 2)