© 2013, 2014 Justis Grant Peters and Sagar Jauhari
Code repository for our project in Educational Data Mining class with Dr. Tiffany Barnes, CSC 591-021 Fall 2013 at NCSU. We are analyzing EEG data collected by Mostow and Chang from Project LISTEN.
$ python process_eeg.py --help
usage: process_eeg.py [-h] -i INDIR -o OUTDIR --type {raw,pam1hz}
[--interpolate] [--kernelsvm] [--chartsforpaper]
[--plotsubjects] [--plotavgrolmed NSAMP]
[--plotavgraw NSAMPRAW] [--plotraw NRAW] [--filter]
[--stats] [--clean]
Process EEG Data.
optional arguments:
-h, --help show this help message and exit
-i INDIR Directory containing EEG Files.
-o OUTDIR Path of processed data and reports. New directory with
timestamp will be created inside this folder.
--type {raw,pam1hz} Select type of input data: Raw 512 Hz data or Neurosky
output data of 1Hz having proprietary fields:
PoorSignal, Attention and Meditation.
--interpolate Interpolate 512Hz time stamps on 512Hz data having 1Hz
time stamps (raw data available from CMU). [raw]
--kernelsvm Perform Kernelized SVC on interpolated raw data. [raw]
--chartsforpaper Create charts in single page PDF files. [raw]
--plotsubjects Create charts for 1Hz data of subjects. [pam1hz]
--plotavgrolmed NSAMP
Plot average rolling median of first NSAMP samples.
Note: Rolling median is usually downsampled to 10Hz,
so 1st second = 10 samples. [raw]
--plotavgraw NSAMPRAW
Plot raw signal averaged over all subjects for 1st
NSAMPRAW samples. [raw]
--plotraw NRAW Plot raw signal of all subjects for 1st NRAW samples.
[raw]
--filter Use Butteworth filter and plot charts for 1Hz subject
data. [pam1hz]
--stats General statistics of data [raw]
--clean Remove data with 0 attention or meditation values;
Remove data with > 0 poor signal value. [pam1hz]
The 512Hz data from CMU has timestamp information, but only at 1Hz resolution. To interpolate these:
python process_series_files.py data/raw/ preprocess/raw/
The Slicer class manages timeseries and extracted features as they relate to tasks from task.xls.
# instantiating task slicer
s = Slicer()
if len(sys.argv) > 2:
print 'loading raw from list of csvfiles'
s.load_series_from_csv('raw', sys.argv[1:])
else:
print 'loading raw from pickle'
s.load_series_from_pickle('raw', sys.argv[1])
# extract features from 'raw'
s.extract_filtered_signal()
s.extract_rolling_median()
s.extract_rolling_PSD()
# fetch task 1, with features
# Return type is a dict, with task-level features, plus the requested timeseries features
print s.get_by_task_id(1, features=['raw','raw_rolling_PSD_512', 'raw_rolling_median_128'])
sudo apt-get install ipython git clone git@github.com:sagarjauhari/edm_eeg.git edm_eeg.git cd edm_eeg.git ipython notebook --pylab-inline
To run an R script, for example sketchpad.R:
cd dir-with-eeg-data/ R --vanilla < /path/to/sketchpad.R
It will produce a PDF called Rplots.pdf with a page for each chart produced by the script.
If you're using eeg_data_set2.zip_, you will want to fix the first line in 20101214171219.1.rawwave.csv to look like this:
%Time,Value
It is a good idea to preprocess the raw files by interpolating the timestamps for the 512Hz data and use these preprocessed files for data analysis.
$ python process_eeg.py -i /media/media/DATA/eeg/eeg_data_set2/ -o /media/media/DATA/eeg/output/preprocess --type raw --interpolate
Now the preprocessed files in /media/media/DATA/eeg/output/preprocess can be used for further analysis.
Since rolling median is downsampled to 10Hz, to analyse the average value of rolling medians of all the samples on the 1st second we need to use the 1st 10 observations.
$ python process_eeg.py -i /media/media/DATA/eeg/eeg_data_set2/preprocess/ -o /media/media/DATA/eeg/output/ --type raw --plotavgrolmed 10
Now lets do the same thing with for the 1st 10 seconds
$ python process_eeg.py -i /media/media/DATA/eeg/eeg_data_set2/preprocess/ -o /media/media/DATA/eeg/output/ --type raw --plotavgrolmed 100
© 2013, 2014 Justis Grant Peters and Sagar Jauhari
This file is part of BCIpy.
BCIpy is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
BCIpy is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with BCIpy. If not, see http://www.gnu.org/licenses/.
- PyEEG - Licenced under GPL V3