Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning

Table 1

Summary of the model architectural parameters.

	Parameter	Values
Input layer	Signal dimension	(6, 2048)
TCN layer	No. of blocks	5
	No. of filters	256
	conv1d	Causal
	Dilation factor	1,2,4
	Kernel	3
	Activation	Gated activation unit
	Spatialdropout1d	0.2
CNN layer	No. of blocks	5
	Convolution	conv1d
	No. of filters	256
	Activation	ReLU
	Dropout	0.1
	Pooling	maxpool1d (2)
Transformation layer	Fully connected	1024
	Activation	ELU
Classification layer	Fully connected	11
	Activation	Softmax
	Loss function	Cross entropy
Training	Optimizer	Adam
	Learning rate	0.00001
	Epochs	300
	Batch size	256