Algorithm 2 Summary of frequency band selection algorithm
Define:  [1] Data vector construction function: FV(X(j), Qi) consisting the operations of constructing new data set by removing the EEG signals of those channels not in Qi.
[2] Iteration stopping criterion: the normalized difference between labels predicted in two successive iterations being less than a predefined threshold δ2.
Input:  the training set and their corresponding labels the test set frequency sub-bands threshold δ1 for stopping the iterations
iter = 0
Fori = 1 to nf
Xi(k) = BF(X(k)), Fi, where .
, where and is a transformation matrix, which is  composed by the first and last three columns of CSP spatial transformation matrix.
SVMtrain by solving Eq. 8
Fork = Nc + 1 to Nc + Nt
end
end
Repeat
iter = iter + 1
Fori = 1 to nf
Ifi =  = 1
with and ,
obtain by Eq. 5
Else
with and ,
obtain by Eq. 5
End
End
corresponding predicted labels
for k = 1 to Nc + Nt
where and is a transformation matrix, which is composed by the first and last three columns of CSP spatial transformation matrix based on
SVMtrain by solving Eq. 8, where yk, are the labels predicted in the previous iteration.
Fork = Nc + 1~to~Nc + Nt
end
Until stopping criterion satisfied
Output: the frequency sub-band F(s) and the corresponding labels