"""Copyright (c) <2009> <Benedikt Zoefel>
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"""
This is an example how to use the functions of the EyemoveCorrectur module
to estimate and remove artefacts caused by eye movements.
"""
import numpy as N
import time
from PyBCI import BCI
from PyBCI.tools.EyemoveCorrector import *
# We'll assume that you have already created a configuration file
example_bci = BCI('BCI.cfg')
# There are four condition we need for an estimation of eye movement artefacts:
conditions = ['resting', 'horizontal', 'vertical', 'blinks']
trials = 20
sec_per_trial = 2
# Make sure that the following matches with your config file.
numof_channels = 5
sample_rate = 500
condition_means = N.zeros((len(conditions), numof_channels))
for num, condition in zip(range(len(conditions)), conditions):
condition_data = N.zeros((trials, numof_channels, sample_rate*sec_per_trial))
for trial in range(trials): # For each trial in each condition...
time.sleep(1) # ...we'll wait a second...
# ...then give a sign by means of a triangle for the beginning of the trial, that is, the data
# collection...
example_bci.trigger_sign('triangle', 0.5, 300)
# ...and store the data, separated for each trial.
condition_data[trial] = example_bci.get_data(sec_per_trial)
print 'Condition', condition, 'done.'
# At last, we'll take the average of the respective condition, but keep
# the channel structure.
condition_data = N.mean(condition_data, axis=2) # average over the samples
condition_data = N.mean(condition_data, axis=0) # average over the trials
# The result is an array with the structure [condition][channels], with the
# channel values averaged over all samples and trials of the respect condition.
condition_means[num] = condition_data
# The next thing we'll do is to estimate the eye movement impact array with the
# collected data:
impact = estimate_impact(condition_means[0], condition_means[1:4])
# That's it! The only thing we'll have to do now is to remove the calculated impact
# by calling the respective function. To finish this script, we'll have
# just a short trial.
for test in range(5):
data = example_bci.get_data(1)
print 'Original data:', data
corr_data = remove_impact(impact, data)
print 'Corrected data:', corr_data
example_bci.end_bci()