y = np.array([ valuedict[val] for val in y ])
# 1: detrend
data = preproc.detrend(data)
# 2: bad-channel removal
goodch, badch = preproc.outlierdetection(data);
data = data[goodch,:,:];
# 3: apply spatial filter
spatialfilter='car'
data = preproc.spatialfilter(data,type=spatialfilter)
# 4: map to frequencies
data,freqs = preproc.powerspectrum(data,dim=1,fSample=fs)
# 5 : select the frequency bins we want
freqbands =[8,10,28,30]
data,freqIdx=preproc.selectbands(data,dim=1,band=freqbands,bins=freqs)
# 6 : bad-trial removal
goodtr, badtr = preproc.outlierdetection(data,dim=2)
data = data[:,:,goodtr]
y = y[goodtr]
# 7: train classifier, default is a linear-least-squares-classifier
clsfr = sklearn.linear_model.LogisticRegressionCV()
X2d = np.reshape(data,(-1,data.shape[2])).T # sklearn needs data to be [nTrials x nFeatures]
clsfr.fit(X2d,y)
print("Best training score=%g"%np.max(np.mean(clsfr.scores_[1],axis=0)))
data = np.array(data) # data contains the signal sampled, 750 samples for 32 channels
data_rec = np.copy(data)
if verbose: print(events_im[0].value) # Information in terminal, verbose option, for debugging purposes
if verbose: print(data.shape) # Information in terminal, verbose option, for debugging purposes
data = data[:,:,:]
if verbose: print(data.shape) # Information in terminal, verbose option, for debugging purposes
data = np.array(data) #data support variable
data = np.transpose(data)
data = preproc.detrend(data); # Preprocessing operations, more detailed in the report
data = preproc.spatialfilter(data, type = 'car') # "
data, freqs = preproc.powerspectrum(data,dim = 1,fSample= 250); # " sample information included in the hdr, must be changed if it changes for it to work
data,freqIdx = preproc.selectbands(data,dim=1,band=[6,13,15,32],bins=freqs); # " the bands selected here also should be the same as the chosen one in the classifier file
if verbose: print(data.shape) # Information in terminal, verbose option, for debugging purposes
data = np.reshape(data, (1,-1)) # Data now, is an bunch of array containing the power spectrum of every channel in the selected frequencies (#ch, #freq), in this line all those arrays become one of size (1, #ch * #freq)
prediction_right = classifier_right.predict(data) # The freq informations is now passed to the classifier, each one yielding a float value
prediction_left = classifier_left.predict(data)
pred = [prediction_left, prediction_right] # The predictions are put into an array, for debugging reasons, this isn't what will be passed
prediction = [np.round(prediction_left).astype(int), np.round(prediction_right).astype(int)] # In this array the predictions are rounded, as the expected values are 0,1 for each classifier but the outputs are float, they need to be rounded
# In the following lunes, the conversion is made from a the array to a single int value, easier to pass as an event value
# stop processing if needed
if "stimulus.feedback" in event_types:
break
# get data in correct format
data = np.transpose(data)
# 1: detrend
data = preproc.detrend(data)
# 2: bad-channel removal (as identifed in classifier training)
data = data[goodch, :, :]
# 3: apply spatial filter (as in classifier training)
data = preproc.spatialfilter(data, type=spatialfilter)
# 4: map to frequencies (TODO: check fs matches!!)
data, freqs = preproc.powerspectrum(data, dim=1, fSample=fs)
# 5: select frequency bins we want
data, freqIdx = preproc.selectbands(data,
dim=1,
band=freqbands,
bins=freqs)
print(data.shape)
# 6: apply the classifier, get raw predictions
X2d = np.reshape(
data,
(-1,
data.shape[2])).T # sklearn needs data to be [nTrials x nFeatures]
fraw = classifier.predict(X2d)
# 7: map from fraw to event values
# predictions = [ ivaluedict[round(i)] for i in fraw ]
predictions = fraw
no_hand_mov = []
for n_exp in range(init, final + 1): # We iterate over all data gathered
if verbose:
print(' *********** Experiment ' + str(n_exp) + ' *********** ')
if verbose: print(np.array(exp[n_exp]['data']).shape)
data = np.array(
exp[n_exp]['data']) # Support variable that contains the data
data = np.transpose(data)
data = preproc.detrend(data)
# Preprocessing functions, for more info check the report
data = preproc.spatialfilter(data, type='car')
data, freqs = preproc.powerspectrum(data,
dim=1,
fSample=exp[i]['hdr'].fSample)
data, freqIdx = preproc.selectbands(data,
dim=1,
band=[6, 13, 15, 32],
bins=freqs)
if verbose: print(data.shape)
n_events = data.shape[2]
for event in range(n_events):
if verbose:
print(exp[n_exp]['events']
[event].value[0]) # Verbose option, for debugging
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
# 2: bad-channel removal
goodch, badch = preproc.outlierdetection(X)
print(goodch)
X = X[goodch, :, :]
# 3: apply spatial filter
X = preproc.spatialfilter(X, 'car')
# 4 & 5: map to frequencies and select frequencies of interest
print(hdr)
X, freqs = preproc.powerspectrum(X, dim=1, fSample=hdr.fSample)
print(freqs)
freqbands = [20, 10, 30, 60]
X, freqIdx = preproc.selectbands(X, dim=1, band=freqbands, bins=freqs)
freqs = freqs[freqIdx]
# 6 : bad-trial removal
goodtr, badtr = preproc.outlierdetection(X, dim=2)
X = X[:, :, goodtr]
labels = labels[goodtr]
# 7: train classifier, default is a linear-least-squares-classifier
import linear
#mapping = {('stimulus.target', 0): 0, ('stimulus.target', 1): 1}
# 2: bad-channel removal goodch, badch = preproc.outlierdetection(X); X = X[goodch,:,:]; Cnames=Cnames[goodch]; Cpos=Cpos[:,goodch]; updatePlots() # 3: apply spatial filter X = preproc.spatialfilter(X,type='car') updatePlots(); # Map to frequency domain, only keep the positive frequencies X,freqs = preproc.powerspectrum(X,dim=1,fSample=fs) yvals = freqs; # ensure the plots use the right x-ticks ylabel='freq (Hz)' updatePlots() # 5 : select the frequency bins we want X,freqIdx=preproc.selectbands(X,dim=1,band=[8,10,28,30],bins=freqs) freqs=freqs[freqIdx] yvals=freqs updatePlots() # 6 : bad-trial removal, trials in dim=2 goodtr, badtr = preproc.outlierdetection(X,dim=2) X = X[:,:,goodtr]