def preprocess(data, events, matlab=False, spectral=True):
'''
preprocessing pipeline for EEG data from the
buffer
Parameters
----------
data : a list of datapoints (numpy arrays)
events : a list of fieldtrip events or a numpy array
spectral: Whether you want to compute a spectrogram (default False)
matlab: Whether the data comes from a .mat file (default False)
Examples
--------
>>> data, events = ftc.getData(0,100)
>>> data, events = preprocess(data, events)
>>> data, events = ftc.getData(0,100)
>>> data, events = preprocess(data, events, spectral=True)
'''
if matlab:
data, events = reformat(data, events)
data = preproc.detrend(data, dim=0)
data, badch = preproc.badchannelremoval(data)
data = preproc.spatialfilter(data, type='whiten')
if spectral:
data = preproc.spectralfilter(data, (1, 40), fSample)
data, events, badtrials = preproc.badtrailremoval(data, events)
return data, events
cname = 'clsfr'
print("Training classifier")
f = pickle.load(open(dname + '.pk', 'rb'))
data = f['data']
events = f['events']
hdr = f['hdr']
# -------------------------------------------------------------------
# Run the standard pre-processing and analysis pipeline
# 1: detrend
data = [d[:, :10] for d in data]
data = preproc.detrend(data)
# 2: bad-channel removal
data, bad_channels = preproc.badchannelremoval(data)
print(f'Removed channels: {bad_channels}')
# 3: apply spatial filter
data = preproc.spatialfilter(data, type='car')
# 4 & 5: map to frequencies and select frequencies of interest
data = preproc.spectralfilter(data, (5, 6, 31, 32), hdr.fSample)
# 6 : bad-trial removal
data, events, bad_trials = preproc.badtrialremoval(data, events)
print(f'Removed trials: {bad_trials}')
with open('processed_data.pkl', 'wb') as f:
pickle.dump({'X': data, 'events': events, 'hdr': hdr}, f)
# Reduce the number of features per channel
freqs = np.linspace(0, hdr.fSample/2, len(data[0]))
data = [d[(5 <= freqs) & (freqs <= 32)] for d in data]
print ("Waiting for startPhase.cmd event.")
while run:
e = bufhelp.waitforevent("startPhase.cmd",1000, True)
print("Got startPhase event: %s"%e)
if e is not None:
if e.value == "calibration":
print("Calibration phase")
data, events, stopevents = bufhelp.gatherdata("stimulus.tgtFlash",trlen_ms,("stimulus.training","end"), milliseconds=True)
pickle.dump({"events":events,"data":data}, open("subject_data", "w"))
elif e.value == "train":
print("Training classifier")
data = preproc.detrend(data)
data, badch = preproc.badchannelremoval(data)
data = preproc.spatialfilter(data)
data = preproc.spectralfilter(data, (0, .1, 10, 12), bufhelp.fSample)
data, events, badtrials = preproc.badtrailremoval(data, events)
mapping = {('stimulus.tgtFlash', '0'): 0, ('stimulus.tgtFlash', '1'): 1}
linear.fit(data,events,mapping)
bufhelp.update()
bufhelp.sendevent("sigproc.training","done")
elif e.value =="testing":
print("Feedback phase")
while True:
data, events, stopevents = bufhelp.gatherdata(["stimulus.columnFlash","stimulus.rowFlash"],trlen_ms,[("stimulus.feedback","end"), ("stimulus.sequence","end")], milliseconds=True)
if isinstance(stopevents, list):
if any(["stimulus.feedback" in x.type for x in stopevents]):
if e.value == "calibration":
print("Calibration phase")
data, events, stopevents = bufhelp.gatherdata(
"stimulus.tgtFlash",
trlen_ms, ("stimulus.training", "end"),
milliseconds=True)
pickle.dump({
"events": events,
"data": data
}, open("subject_data", "w"))
elif e.value == "train":
print("Training classifier")
data = preproc.detrend(data)
data, badch = preproc.badchannelremoval(data)
data = preproc.spatialfilter(data)
data = preproc.spectralfilter(data, (0, .1, 10, 12),
bufhelp.fSample)
data, events, badtrials = preproc.badtrailremoval(data, events)
mapping = {
('stimulus.tgtFlash', '0'): 0,
('stimulus.tgtFlash', '1'): 1
}
linear.fit(data, events, mapping)
bufhelp.update()
bufhelp.sendevent("sigproc.training", "done")
elif e.value == "testing":
print("Feedback phase")
while True:
print("Calibration phase")
data, events, stopevents = bufhelp.gatherdata(
"stimulus.hybrid",
trlen_ms, ("stimulus.training", "end"),
milliseconds=True)
pickle.dump({
"events": events,
"data": data
}, open("subject_data", "wb"))
elif e.value == "training":
print("Training classifier")
data = preproc.detrend(data)
data, badch = preproc.badchannelremoval(data)
data = preproc.spatialfilter(data)
data = preproc.spectralfilter(data, (1, 10, 15, 25),
bufhelp.fSample)
data, events, badtrials = preproc.badtrailremoval(data, events)
mapping = {
('stimulus.hybrid', 'left'): -1,
('stimulus.hybrid', 'right'): 1
}
classifier = linear.fit(data, events, mapping)
bufhelp.update()
bufhelp.sendEvent("sigproc.training", "done")
elif e.value == "contfeedback":
print("Feedback phase")
while True:
for element in original:
if hasattr(element,"deepcopy"):
clone.append(element.deepcopy())
elif hasattr(element,"copy"):
clone.append(element.copy())
else:
clone.append(element)
return clone
if __name__=="main":
#%pdb
import preproc
import numpy
fs = 40
data = numpy.random.randn(3,fs,5) # 1s of data
ppdata=preproc.detrend(data,0)
ppdata=preproc.spatialfilter(data,'car')
ppdata=preproc.spatialfilter(data,'whiten')
ppdata,goodch=preproc.badchannelremoval(data)
ppdata,goodtr=preproc.badtrialremoval(data)
goodtr,badtr=preproc.outlierdetection(data,-1)
Fdata,freqs=preproc.fouriertransform(data, 2, fSamples=1, posFreq=True)
Fdata,freqs=preproc.powerspectrum(data, 1, dim=2)
filt=preproc.mkFilter(10,[0,4,6,7],1)
filt=preproc.mkFilter(numpy.abs(numpy.arange(-10,10,1)),[0,4,6,7])
ppdata=preproc.fftfilter(data,1,[0,3,4,6],fs)
ppdata,keep=preproc.selectbands(data,[4,5,10,15],1); ppdata.shape
