| Title: | Electroencephalography Toolkit Datasets |
|---|---|
| Description: | Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository. |
| Authors: | Nathaniel E. Helwig <[email protected]> |
| Maintainer: | Nathaniel E. Helwig <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1 |
| Built: | 2024-12-15 07:30:33 UTC |
| Source: | CRAN |
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
The data file eegdata contains 64-channel EEG data recorded from 10 alcoholic and 10 control subjects. The funtion geteegdata can be used to create larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Nathaniel E. Helwig <[email protected]>
Maintainer: Nathaniel E. Helwig <[email protected]>
Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Begleiter, H. Neurodynamics Laboratory. State University of New York Health Center at Brooklyn.
Helwig, N.E. (2014). eegkit: Toolkit for electroencephalography data. http://CRAN.R-project.org/package=eegkit
Ingber, L. (1997). Statistical mechanics of neocortical interactions: Canonical momenta indicatros of electroencephalography. Physical Review E, 55, 4578-4593.
Ingber, L. (1998). Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG. Mathematical Computer Modelling, 27, 33-64.
# See examples for eegcap, eegtime, eegspace, eegica, and eegsmooth (in package eegkit)# See examples for eegcap, eegtime, eegspace, eegica, and eegsmooth (in package eegkit)
Contains 64-channel electroencephalography (EEG) data from 10 alcoholic and 10 control subjects participating in a visual event-related potential (ERP) experiment. Data frame contains 5 trials (replications) from each subject. Data were recorded at 256 Hz for 1 second following the presentation of the visual stimulus.
data(eegdata)data(eegdata)
A data frame with 1638400 observations and the following 7 variables:
Subject identification numbers (factor).
Subject group: "a" for alcoholic and "c" for control (factor).
Experimental condition: "S1" (factor).
Trial number for each replication (integer).
Channel from which data was recorded (factor).
Time point at which data was recorded: 0,1,...,255 (integer).
Recorded EEG voltage in microvolts (numeric).
Created from UCI MLR EEG training data using geteegdata with option nt=5.
Nathaniel E. Helwig <[email protected]>
Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Begleiter, H. Neurodynamics Laboratory. State University of New York Health Center at Brooklyn.
Ingber, L. (1997). Statistical mechanics of neocortical interactions: Canonical momenta indicatros of electroencephalography. Physical Review E, 55, 4578-4593.
Ingber, L. (1998). Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG. Mathematical Computer Modelling, 27, 33-64.
# see examples for eegtime, eegspace, eegica, and eegsmooth (in package eegkit) # example code to create eegdata (not run): # #(1)# download and untar SMNI_CMI_TRAIN.tar.gz file from UCI: # # http://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/ # #(2)# eegdata=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TRAIN/",nt=5)# see examples for eegtime, eegspace, eegica, and eegsmooth (in package eegkit) # example code to create eegdata (not run): # #(1)# download and untar SMNI_CMI_TRAIN.tar.gz file from UCI: # # http://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/ # #(2)# eegdata=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TRAIN/",nt=5)
Creates a data matrix (observations by variables) from the EEG Database on UCI Machine Learning Repository. Data matrix has 7 variables: subject, group, condition, trial, channel, time, and voltage. See eegdata and Details for more information.
geteegdata(indir, outdir = indir, cond = c("S1", "S2m", "S2n"), nt = NULL, filename = "eegdata", filetype = c(".rda", ".csv", ".txt"))geteegdata(indir, outdir = indir, cond = c("S1", "S2m", "S2n"), nt = NULL, filename = "eegdata", filetype = c(".rda", ".csv", ".txt"))
indir |
Input directory (containing EEG data source folders). |
outdir |
Output directory (to save EEG data matrix file). |
cond |
Condition to read-in: S1=single stimulus, S2m=two matching stimuli, S2n=two non-matching stimuli. |
nt |
Number of trials to read-in for each subject (default is all trials). |
filename |
Name for EEG data matrix (default |
filetype |
Type of file to save (default is R data file .rda). |
EEG Database on UCI website contains 64-channel electroencephalography (EEG) data from alcoholic and control subjects participating in a visual event-related potential (ERP) experiment. Subjects were exposed to three experimental conditions: S1 single visual stimulus, S2m two matching visual stimuli, S2n two non-matching visual stimuli. Each subject participated in multiple trials (replications) of each experimental condition. Data were recorded at 256 Hz for 1 second following the presentation of the visual stimulus/stimuli.
Creates and saves a data matrix file.
Nathaniel E. Helwig <[email protected]>
Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Begleiter, H. Neurodynamics Laboratory. State University of New York Health Center at Brooklyn.
Ingber, L. (1997). Statistical mechanics of neocortical interactions: Canonical momenta indicatros of electroencephalography. Physical Review E, 55, 4578-4593.
Ingber, L. (1998). Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG. Mathematical Computer Modelling, 27, 33-64.
########## EXAMPLE 1: UCI TRAIN DATA (not run) ########## # Note: you need to change 'indir' and 'outdir' in Steps 2-4 # #(1)# download and untar SMNI_CMI_TRAIN.tar.gz file from UCI: # # # http://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/ ##### for Unix/Mac ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S1",filename="eegtrainS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S2m",filename="eegtrainS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S2n",filename="eegtrainS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ##### for Windows ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S1",filename="eegtrainS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S2m",filename="eegtrainS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S2n",filename="eegtrainS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ########## EXAMPLE 2: UCI TEST DATA (not run) ########## # # Note: you need to change 'indir' and 'outdir' in Steps 2 and 3 # #(1)# download and untar SMNI_CMI_TEST.tar.gz file from UCI: # # # http://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/ ##### for Unix/Mac ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S1",filename="eegtestS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S2m",filename="eegtestS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S2n",filename="eegtestS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ##### for Windows ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S1",filename="eegtestS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S2m",filename="eegtestS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S2n",filename="eegtestS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ########## EXAMPLE 3: UCI FULL DATA (not run) ########## # #(1)# download and untar eeg_full.tar file from UCI: # # # http://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/ ##### for Unix/Mac ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="/Users/Nate/Downloads/eeg_full/", # cond="S1",filename="eegfullS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="/Users/Nate/Downloads/eeg_full/", # cond="S2m",filename="eegfullS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="/Users/Nate/Downloads/eeg_full/", # cond="S2n",filename="eegfullS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ##### for Windows ##### # #(2)# extract all conditions and save as .rda (default use) # eegS1=geteegdata(indir="C:/Users/Nate/Downloads/eeg_full/", # cond="S1",filename="eegfullS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="C:/Users/Nate/Downloads/eeg_full/", # cond="S2m",filename="eegfullS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="C:/Users/Nate/Downloads/eeg_full/", # cond="S2n",filename="eegfullS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n)########## EXAMPLE 1: UCI TRAIN DATA (not run) ########## # Note: you need to change 'indir' and 'outdir' in Steps 2-4 # #(1)# download and untar SMNI_CMI_TRAIN.tar.gz file from UCI: # # # http://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/ ##### for Unix/Mac ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S1",filename="eegtrainS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S2m",filename="eegtrainS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S2n",filename="eegtrainS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ##### for Windows ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S1",filename="eegtrainS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S2m",filename="eegtrainS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TRAIN/", # cond="S2n",filename="eegtrainS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ########## EXAMPLE 2: UCI TEST DATA (not run) ########## # # Note: you need to change 'indir' and 'outdir' in Steps 2 and 3 # #(1)# download and untar SMNI_CMI_TEST.tar.gz file from UCI: # # # http://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/ ##### for Unix/Mac ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S1",filename="eegtestS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S2m",filename="eegtestS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S2n",filename="eegtestS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ##### for Windows ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S1",filename="eegtestS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S2m",filename="eegtestS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="C:/Users/Nate/Downloads/SMNI_CMI_TEST/", # cond="S2n",filename="eegtestS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ########## EXAMPLE 3: UCI FULL DATA (not run) ########## # #(1)# download and untar eeg_full.tar file from UCI: # # # http://archive.ics.uci.edu/ml/machine-learning-databases/eeg-mld/ ##### for Unix/Mac ##### # #(2)# extract condition "S1" and save as .rda # eegS1=geteegdata(indir="/Users/Nate/Downloads/eeg_full/", # cond="S1",filename="eegfullS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="/Users/Nate/Downloads/eeg_full/", # cond="S2m",filename="eegfullS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="/Users/Nate/Downloads/eeg_full/", # cond="S2n",filename="eegfullS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n) ##### for Windows ##### # #(2)# extract all conditions and save as .rda (default use) # eegS1=geteegdata(indir="C:/Users/Nate/Downloads/eeg_full/", # cond="S1",filename="eegfullS1") # #(3)# extract condition "S2m" and save as .rda # eegS2m=geteegdata(indir="C:/Users/Nate/Downloads/eeg_full/", # cond="S2m",filename="eegfullS2m") # #(4)# extract condition "S2n" and save as .rda # eegS2n=geteegdata(indir="C:/Users/Nate/Downloads/eeg_full/", # cond="S2n",filename="eegfullS2n") # #(5)# combine conditions # eegdata=rbind(eegS1,eegS2m,eegS2n)