Nothing
classifyEEG <-
function(y,data){
LL<-y$L
nel=y$nch
NN<-nrow(data)
if(NN<LL) data<-rbind(matrix(rep(0,(LL-NN)*nel),ncol=nel),data) else {
data<-data[(NN-LL+1):NN,]
}
featype<-y$featype
#####SPECTRO
if (sum(c("f3","f4")%in%featype)>0) {
specdata<-.spec.pgram(data)
}
####PCA
if (sum(c("f2","f5")%in%featype)>0) {
ncomps<-y$ncomps
rot<-eigen(var(data))$vectors[,1:ncomps]
loaddata<-rot
pcadata<-as.matrix(data)%*%rot
}
####PCA DO SPEC
if (c("f4")%in%featype) {
ncomps<-y$ncomps
rot<-eigen(var(specdata))$vectors[,1:ncomps]
loadspecdata<-rot
pcaspecdata<-specdata%*%rot
}
####WAVELET
if (c("f7")%in%featype) {
x<-as.matrix(wavCWT(1:LL,wavelet=y$wavelet,variance=y$variance))
L0<-nrow(x)*ncol(x)
cwtdata <- mat.or.vec(L0,nel)
feacwt<-which(featype=="f7")
wcwt<-c()
for(i in feacwt){
wcwt<-c(wcwt,y$W[[i]])
}
wcwt<-unique(floor((wcwt-1)/L0)+1)
for (el in wcwt){
cwtdata[,el]<-abs(as.vector(wavCWT(data[,el],wavelet=y$wavelet,variance=y$variance)))
}
}
L<-y$L
contwin<-0
contW<-0
contFea<-1
features<-numeric(y$nfea)
win<-y$win
stat<-y$stat
mintomax<-y$mintomax
log<-y$log
abs<-y$abs
power<-y$power
W<-y$W
nel<-y$nch
for (FEA in featype){
if (FEA=="f1"){
contW<-contW+1
contwin<-contwin+1
Ww<-W[[contW]]
log[contwin]
features[contFea:(contFea+length(Ww)-1)]<-.winPrac(data,win[contwin],stat[contwin],power[contwin],abs[contwin],log[contwin],mintomax[contwin],Ww,nel)
contFea<-contFea+length(Ww)
}
if (FEA=="f2"){
contW<-contW+1
Ww<-W[[contW]]
features[contFea:(contFea+length(Ww)-1)]<-abs(loaddata[Ww])
contFea<-contFea+length(Ww)
}
if (FEA=="f3"){
contW<-contW+1
contwin<-contwin+1
Ww<-W[[contW]]
features[contFea:(contFea+length(Ww)-1)]<-.winPrac(specdata,win[contwin],
stat[contwin],power[contwin],abs[contwin],log[contwin],mintomax[contwin],Ww,nel)
contFea<-contFea+length(Ww)
}
if (FEA=="f4"){
contW<-contW+1
Ww<-W[[contW]]
features[contFea:(contFea+length(Ww)-1)]<-abs(loadspecdata[Ww])
contFea<-contFea+length(Ww)
}
if (FEA=="f5"){
contW<-contW+1
contwin<-contwin+1
Ww<-W[[contW]]
features[contFea:(contFea+length(Ww)-1)]<-.winPrac(pcadata,win[contwin],stat[contwin],power[contwin],abs[contwin],log[contwin],mintomax[contwin],Ww,ncomps)
contFea<-contFea+length(Ww)
}
if (FEA=="f6"){
contW<-contW+1
contwin<-contwin+1
Ww<-W[[contW]]
dados<-.winPrac2(data,win[contwin],stat[contwin],power[contwin],abs[contwin],log[contwin],mintomax[contwin],nel,L)
contwin<-contwin+1
features[contFea:(contFea+length(Ww)-1)]<-.winPrac(dados,win[contwin],stat[contwin],power[contwin],abs[contwin],log[contwin],mintomax[contwin],Ww,nel)
contFea<-contFea+length(Ww)
}
if (FEA=="f7"){
contW<-contW+1
Ww<-W[[contW]]
features[contFea:(contFea+length(Ww)-1)]<-as.vector(cwtdata)[Ww]
contFea<-contFea+length(Ww)
}
} #for FEA in featype
m<-y$model
Ans<-predict(m, t(as.matrix(features)))
prob <- predict(m, t(as.matrix(features)), probability = TRUE)
prob<-attr(prob,"probabilities")
NCL<-y$model$nclasses
wc<-y$which.classes
if(NCL==2){
if (Ans=="A") pred<-c(prob[1],wc[1]) else pred<-c(prob[2],wc[2])
}else{
if (Ans=="A") pred<-c(prob[1],wc[1]) else if (Ans=="B") pred<-c(prob[2],wc[2]) else pred<-c(prob[3],wc[3])
}
return(pred)
}
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