Explainable electroencephalography-based attention-deficit/hyperactivity disorder detection model with a combination of ternary pattern and twin wavelet transform

Table 2 Details of various components of the proposed model

Layer	Method(s)	Parameter(s)	Output(s)
Feature extraction	Twin wavelet transformation	Filter: Sym4; number of levels: 7	13 wavelet bands
	Combination ternary pattern	Kernel: Ternary; block length: 5; pattern: Combination	Feature vector length: 486
	Statistical features	20 statistical moments	Feature vector length: 40
	Concatenate features	Textural and statistical features	Feature vector length: 526
	Concatenate feature vectors	Merge 14 feature vectors	Feature vector length: 7364
Feature selection	Neighborhood component analysis	Select the top informative features	Selected feature vector length: 263
Classification	k-Nearest neighbors	k:1; distance: L2norm, voting: No	20 prediction vectors
Post-processing	Iterative majority voting	Loop range: 3 to 20; function: Mode	18 voted vectors
	Greedy algorithm	Maximum accuracy	Best result