Thetazero Pubs
Chi Lin




6 6, 2016

Outline : From User's Perspective

Data

1. ERPdata

Preprocessing

1. data_select
2. data_summarize
3. downsample

Exploratory Data Analysis

1.edaplot
2.ciplot
3.scalp_plot

Analysis Plotting

1. "ERP" package (Causeur, Chu, Hsieh, & Sheu, 2012)
2. mcplot
3. coord_plot

Data

head(dta[,1:6],5)
  Subject Channel Condition   value.1   value.2   value.3
1   subj1     Fp1   nonword -1.545226 -1.889412 -1.463414
2   subj1     Fp2   nonword -1.373787 -1.853133 -1.751572
3   subj1      F3   nonword -2.856959 -2.690639 -0.926768
4   subj1      F4   nonword -2.302168 -2.675416 -1.816674
5   subj1      C3   nonword -4.778307 -4.948525 -2.399150
tail(dta[,1:6],5)
     Subject Channel Condition   value.1    value.2    value.3
1356   subj9     FT7      word  1.809377  2.3673330  1.6798440
1357   subj9     FT8      word  1.007716  1.2813160  1.0245420
1358   subj9      A2      word -1.071328 -0.8085886  0.2553779
1359   subj9   VEOG1      word 17.208140 24.4108600 24.2048600
1360   subj9   HEOG1      word  2.666729  4.6142750  4.8465790

Data

str(dta[,1:9],digits.d=1)
'data.frame':   1360 obs. of  9 variables:
 $ Subject  : Factor w/ 20 levels "subj1","subj2",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Channel  : Factor w/ 34 levels "Fp1","Fp2","F3",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ Condition: Factor w/ 2 levels "nonword","word": 1 1 1 1 1 1 1 1 1 1 ...
 $ value.1  : num  -2 -1 -3 -2 -5 ...
 $ value.2  : num  -2 -2 -3 -3 -5 ...
 $ value.3  : num  -1.5 -1.8 -0.9 -1.8 -2.4 ...
 $ value.4  : num  -1.3 -1.9 -0.2 -1.5 -1.3 ...
 $ value.5  : num  -1 -1.6 0.8 -0.8 0.5 ...
 $ value.6  : num  -1.7 -1.6 0.3 -0.8 1.1 ...

Look at Data : Overall

Look at Data : Detail,

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