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Museprocessing_epoching
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Museprocessing_epoching
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library(eegkit)
library(rhdf5)
library(ggplot2)
#import data
first <- h5read("1st.h5", "/IXDATA/raw/")
second <- h5read("2nd.h5", "/IXDATA/raw/")
third <- h5read("3rd.h5", "/IXDATA/raw/")
time1 <- first$eeg$times
time2 <- second$eeg$times
time3 <- third$eeg$times
#prepare raw eeg
first_eeg <- as.data.frame(first$eeg$data)
second_eeg <- as.data.frame(second$eeg$data)
third_eeg <- as.data.frame(third$eeg$data)
colnames(first_eeg) <- c('TP9', 'FP1', 'FP2', 'TP10')
colnames(second_eeg) <- c('TP9', 'FP1', 'FP2', 'TP10')
colnames(third_eeg) <- c('TP9', 'FP1', 'FP2', 'TP10')
wd <- getwd()
#alpha_psd_first <- eegfft(x = first_eeg, Fs = 220, lower = 7.5, upper = 13)
#alpha_psd_second <- eegfft(x = second_eeg, Fs = 220, lower = 7.5, upper = 13)
#alpha_psd_third <- eegfft(x = third_eeg, Fs = 220, lower = 7.5, upper = 13)
setwd("D:/WillOwl")
#prepare time data
time.first <- read.csv(file = 'time_first.csv', header = F,
sep = ';', encoding = 'UTF-8')
time.second <- read.csv(file = 'time_second.csv', header = F,
sep = ';', encoding = 'UTF-8')
time.third <- read.csv(file = 'time_third.csv', header = F,
sep = ';', encoding = 'UTF-8')
time.first[,1] <- as.numeric(levels(time.first[,1]))[time.first[,1]]
time.first[1,1] <- 1
time.second[,1] <- as.numeric(levels(time.second[,1]))[time.second[,1]]
time.second[1,1] <- 1
time.third[,1] <- as.numeric(levels(time.third[,1]))[time.third[,1]]
time.third[1,1] <- 1
setwd(wd)
#identification of epochs
time1 <- as.data.frame(sapply(time1, function(x) floor(x)))
t1 <- data.frame(matrix(NA, nrow = nrow(time1), ncol=1))
time1 <- as.data.frame(time1[-c(which(time1[,1]> time1[1,1]+nrow(time.first)-1)),])
#i <- 1333
#t1[,1] <-
#apply(time1, 1, function(i) time.first[which(time.first[,1]==sum(time1[i,1],-time1[1,1],1)),2])
#t1 <- apply(time1, 1, function(i) as.numeric(time.first[time.first[,1]==sum(time1[i,1],-time1[1,1],1),2]))
for (i in 1:nrow(time1)) {
mom <- time1[i,1]
index2 <- sum(mom,-time1[1,1],1)
t1[i,1] <- as.numeric(time.first[time.first[,1]==index2,2])
}
time2 <- as.data.frame(sapply(time2, function(x) floor(x)))
time2 <- as.data.frame(time2[-c(which(time2[,1]> time2[1,1]+nrow(time.second)-1)),])
t2 <- data.frame(matrix(NA, nrow = nrow(time2), ncol=1))
for (i in 1:nrow(time2)) {
mom <- time2[i,1]
index2 <- sum(mom,-time2[1,1],1)
t2[i,1] <- as.numeric(time.second[time.second[,1]==index2,2])
}
time3 <- as.data.frame(sapply(time3, function(x) floor(x)))
time3 <- as.data.frame(time3[-c(which(time3[,1]> time3[1,1]+nrow(time.third)-1)),])
t3 <- data.frame(matrix(NA, nrow = nrow(time3), ncol=1))
for (i in 1:nrow(time3)) {
mom <- time3[i,1]
index2 <- sum(mom,-time3[1,1],1)
t3[i,1] <- as.numeric(time.third[time.third[,1]==index2,2])
}
#mariking epoches
first_eeg[1:nrow(t1), 5] <- t1[,1]
first_eeg <- as.data.frame(first_eeg[-c(nrow(t1)+1:nrow(first_eeg)),])
second_eeg[1:nrow(t2), 5] <- t2[,1]
second_eeg <- as.data.frame(second_eeg[-c(nrow(t2)+1:nrow(second_eeg)),])
third_eeg[1:nrow(t3), 5] <- t3[,1]
third_eeg <- as.data.frame(third_eeg[-c(nrow(t3)+1:nrow(third_eeg)),])
#fft for each category of pictures
p1 <- first_eeg[which(first_eeg[,5]==1),1:4]
n1 <- first_eeg[which(first_eeg[,5]==0),1:4]
p1.fft <- eegfft(x = p1, Fs = 220, lower = 7.5, upper = 13)
p1.TP <- log(p1.fft$strength[,1]) - log(p1.fft$strength[,4])
mean(p1.TP)
p1.FP <- log(p1.fft$strength[,2]) - log(p1.fft$strength[,3])
mean(p1.FP)
n1.fft <- eegfft(x = n1, Fs = 220, lower = 7.5, upper = 13)
n1.TP <- log(n1.fft$strength[,1]) - log(n1.fft$strength[,4])
mean(n1.TP)
n1.FP <- log(n1.fft$strength[,2]) - log(n1.fft$strength[,3])
mean(n1.FP)
p2 <- second_eeg[which(second_eeg[,5]==1),1:4]
s2 <- second_eeg[which(second_eeg[,5]==2),1:4]
p2.fft <- eegfft(x = p2, Fs = 220, lower = 7.5, upper = 13)
p2.TP <- log(p2.fft$strength[,1]) - log(p2.fft$strength[,4])
mean(p2.TP)
p2.FP <- log(p2.fft$strength[,2]) - log(p2.fft$strength[,3])
mean(p2.FP)
s2.fft <- eegfft(x = s2, Fs = 220, lower = 7.5, upper = 13)
s2.TP <- log(s2.fft$strength[,1]) - log(s2.fft$strength[,4])
mean(s2.TP)
s2.FP <- log(s2.fft$strength[,2]) - log(s2.fft$strength[,3])
mean(s2.FP)
p3 <- third_eeg[which(third_eeg[,5]==1),1:4]
n3 <- third_eeg[which(third_eeg[,5]==0),1:4]
p3.fft <- eegfft(x = p3, Fs = 220, lower = 7.5, upper = 13)
p3.TP <- log(p3.fft$strength[,1]) - log(p3.fft$strength[,4])
mean(p3.TP)
p3.FP <- log(p3.fft$strength[,2]) - log(p3.fft$strength[,3])
mean(p3.FP)
n3.fft <- eegfft(x = n3, Fs = 220, lower = 7.5, upper = 13)
n3.TP <- log(n3.fft$strength[,1]) - log(n3.fft$strength[,4])
mean(n3.TP)
n3.FP <- log(n3.fft$strength[,2]) - log(n3.fft$strength[,3])
mean(n3.FP)
result1 <- rbind(c('positive TP', 'positive FP'),
c(mean(p1.TP), mean(p1.FP)),
c('negative TP', 'negative FP'),
c(mean(n1.TP), mean(n1.FP)))
png(filename="a_positive1.png")
eegpsd(p1, Fs=220, lower = 7.5, upper = 13)
dev.off()
png(filename="a_negative1.png")
eegpsd(n1, Fs=220, lower = 7.5, upper = 13)
dev.off()
result2 <- rbind(c('positive TP', 'positive FP'),
c(mean(p2.TP), mean(p2.FP)),
c('stress TP', 'stress FP'),
c(mean(s2.TP), mean(s2.FP)))
png(filename="a_positive2.png")
eegpsd(p2, Fs=220, lower = 7.5, upper = 13)
dev.off()
png(filename="a_stress2.png")
eegpsd(s2, Fs=220, lower = 7.5, upper = 13)
dev.off()
result3 <- rbind(c('positive TP', 'positive FP'),
c(mean(p3.TP), mean(p3.FP)),
c('negative TP', 'negative FP'),
c(mean(n3.TP), mean(n3.FP)))
png(filename="a_positive3.png")
eegpsd(p3, Fs=220, lower = 7.5, upper = 13)
dev.off()
png(filename="a_negative3.png")
eegpsd(n3, Fs=220, lower = 7.5, upper = 13)
dev.off()
write.csv(x = result1, file = 'result1.csv')
write.csv(x = result2, file = 'result2.csv')
write.csv(x = result3, file = 'result3.csv')
rm(list=ls())