Graphical Exploratory Tools for ERP
Chi-Lin Yu
Outline
A reason workflow from user's perspective
Data
1. ERPdata : maybe a formal data for package
Preprocessing
1. data_select
2. data_summarize
3. downsample
EDA
1.edaplot
Data
head(dta[,1:8],5)
Channel Experiment Condition Subject AveRT IQ value.1 value.2
13 T3 Exp1 nonword subj1 0.01340599 65 1.809247 1.9723400
14 T4 Exp1 nonword subj1 0.01340599 65 1.064528 1.1616680
15 T5 Exp1 nonword subj1 0.01340599 65 -2.184362 -2.2619550
16 T6 Exp1 nonword subj1 0.01340599 65 0.134014 0.4524255
17 CZ Exp1 nonword subj1 0.01340599 65 -2.501931 -3.2716440
tail(dta[,1:8],5)
Channel Experiment Condition Subject AveRT IQ value.1 value.2
1344 FZ Exp1 word subj9 4.452716 84 1.763616 2.512715
1345 PZ Exp1 word subj9 4.452716 84 2.450751 3.467061
1346 FCZ Exp1 word subj9 4.452716 84 2.039289 2.840635
1347 CPZ Exp1 word subj9 4.452716 84 2.420711 3.365350
1348 CP3 Exp1 word subj9 4.452716 84 2.272059 3.157104
Data
str(dta[,1:9],digits.d=1)
'data.frame': 200 obs. of 9 variables:
$ Channel : Factor w/ 10 levels "CP3","CPZ","CZ",..: 7 8 9 10 3 5 6 4 2 1 ...
$ Experiment: chr "Exp1" "Exp1" "Exp1" "Exp1" ...
$ Condition : Factor w/ 2 levels "nonword","word": 1 1 1 1 1 1 1 1 1 1 ...
$ Subject : Factor w/ 10 levels "subj1","subj2",..: 1 1 1 1 1 1 1 1 1 1 ...
$ AveRT : num 0.01 0.01 0.01 0.01 0.01 ...
$ IQ : num 65 65 65 65 65 ...
$ value.1 : num 1.8 1.1 -2.2 0.1 -2.5 ...
$ value.2 : num 2 1.2 -2.3 0.5 -3.3 ...
$ value.3 : num 0.9 0.7 -1.3 0.6 -2.7 ...
Look at Data : Overall
timepoint <- seq(0,1700,by=4)
Fig0 <- edaplot(data = dta,
frames = timepoint ,
datacol=7:433,
subjcol=4,
chancol=1,
othvarcol=c(2:3,5:6))+
geom_line(aes(col=Condition))+
facet_grid(Channel~Subject)
Look at Data : Overall
Look at Data : Detail
timepoint <- seq(0,1700,by=4)
Fig1 <- edaplot(data = dta,
frames = timepoint ,
datacol=7:432,
subjcol=4,
chancol=1,
othvarcol=c(2:3,5:6),
select_subj = c("subj2","subj10"),
select_chan = c("PZ","CPZ"))+
geom_line(aes(col=Condition))+
facet_grid(Channel~Subject)+
theme_bw()
Look at Data : Detail
Look at Data : Detail
timepoint <- seq(0,1700,by=4)
Fig2 <- edaplot(data = dta,
frames = timepoint ,
datacol=7:432,
subjcol=4,
chancol=1,
othvarcol=c(2:3,5:6),
select_chan = c("PZ","CPZ"),
outlinesub = "subj2",
outcolor = "red")+
facet_grid(.~Channel)+
theme_bw()
Fig3 <- edaplot(data = dta,
frames = timepoint ,
datacol=7:432,
subjcol=4,
chancol=1,
othvarcol=c(2:3,5:6),
select_chan = c("PZ","CPZ"),
outlinesub = "subj10",
outcolor = "green")+
facet_grid(.~Channel)+
theme_bw()
Look at Data : Detail
Data Selection : Delete Subject 2 & 10
dta2 <- data_select(data = dta,
frames = timepoint ,
datacol=7:432,
subjcol=4,
chancol=1,
othvarcol=c(2:3,5:6),
select_subj = c(paste("subj",c(1,3:9),sep="")))
head(dta2[,1:8])
Channel Experiment Condition Subject AveRT IQ value.1 value.2
1 T3 Exp1 nonword subj1 0.01340599 65 1.809247 1.9723400
2 T4 Exp1 nonword subj1 0.01340599 65 1.064528 1.1616680
3 T5 Exp1 nonword subj1 0.01340599 65 -2.184362 -2.2619550
4 T6 Exp1 nonword subj1 0.01340599 65 0.134014 0.4524255
5 CZ Exp1 nonword subj1 0.01340599 65 -2.501931 -3.2716440
6 FZ Exp1 nonword subj1 0.01340599 65 -2.422126 -2.8691950
Downsample : smoothing data
dta3 <- downsample(data = dta2,
datacol = 7:432,
binwidth = 10,
movinginterval=5)
timepoint2 <- seq(1,1700,by = 16.1)
Fig4 <- edaplot(data = dta3,
frames = timepoint2 ,
datacol= 7:112,
subjcol=4,
chancol=1,
othvarcol=c(2:3,5:6))+
geom_line(aes(col=Subject))+
facet_wrap(~Channel)
Downsample:smoothing data
Data Exploration : Focus on "Condition"
Fig5 <- edaplot(data = dta3,
frames = timepoint2 ,
datacol= 7:112,
subjcol=4,
chancol=1,
othvarcol=c(2:3,5:6))+
geom_line(aes(col=Condition))+
facet_wrap(~Channel)+
stat_summary(aes(group=Condition,shape=Condition),
fun.y = "mean",col = "purple",size = 1, geom = "point")+
theme_bw()+
theme(legend.position="bottom")+
labs(list(x="Signal",y="Time Points"))
Data Exploration: Focus on "Condition"
Data Exploration : Focus on "IQ"
Fig6 <- edaplot(data = dta3,
frames = timepoint2 ,
datacol= 7:112,
subjcol=4,
chancol=1,
othvarcol=c(2:3,5:6))+
geom_line(aes(col=IQ))+
facet_wrap(~Channel)+
scale_color_continuous(low="pink",high="red")+
theme_bw()
Data Exploration : Focus on "IQ"
Data Summarize : by "Condition"
#head(dta3[1:8])
dtasum <- data_summarize(data=dta3,
frames = timepoint2,
datacol = c(7:112,5,6),
subjcol = 4,
chancol = 1,
othvarcol = c(2:3,5:6),
summarycol= c(1,3),
fun=mean)
head(dtasum[,c(1:3,108:110)])
Channel Condition value.1 value.106 AveRT IQ
1 CP3 nonword 0.28431756 -1.7114410 2.5333 78.375
2 CPZ nonword 0.32909478 0.6572579 2.5333 78.375
3 CZ nonword 0.14390679 0.2463680 2.5333 78.375
4 FCZ nonword 0.05660090 -1.0273331 2.5333 78.375
5 FZ nonword -0.05661048 -2.1258145 2.5333 78.375
6 PZ nonword 0.48773763 0.1641567 2.5333 78.375
Average Data Exploration : Focus on "aveRT", "Condition"
Fig7 <- edaplot(data = dtasum,
frames = timepoint2 ,
datacol= 3:108,
subjcol=NULL,
chancol=1,
othvarcol=c(2,109:110))+
geom_line(aes(col=Condition,size=as.factor(round(AveRT,1))))+
facet_wrap(~Channel)+
theme_bw()+
scale_size_discrete(name="Average RT",range=c(1,2))+
theme(legend.position="bottom")+
labs(list(title="Average ERP curve",x="Signal",y="Time Points"))
Average Data Exploration : Focus on "aveRT", "Condition"
Average Data Exploration : PZ, T3, T4
Fig8 <- edaplot(data = dtasum,
frames = timepoint2 ,
datacol= 3:108,
subjcol=NULL,
chancol=1,
othvarcol=c(2,109:110),
select_chan = c("T3","T4","PZ"))+
geom_line(aes(col=Condition))+
facet_grid(Channel~.)+
theme_bw()+
theme(legend.position="bottom")+
labs(list(title="Average ERP curve",x="Signal",y="Time Points"))
Average Data Exploration : PZ, T3, T4
Average Data Exploration : Ready for further analysis
Fig9 <- Fig8+geom_vline(xintercept = c(400:450),col="pink",alpha=0.1)+
theme(legend.position=c(0.7,0.95),legend.direction="horizontal",
legend.key.size =unit(0.3, "cm"))
Average Data Exploration : Ready for further analysis
