def freqfilt_eeg(fn_in,fn_out,btype="lp",fl=None,fh=None,border=3,windowed=False):
eeg = eegpy.open_eeg(fn_in)
out = eegpy.F32(fn_out,"w+",shape=eeg.shape,cNames=eeg.channel_names,Fs=eeg.Fs)
if btype == "lp":
if not windowed:
out[:,:] = filtfilt_low(fl,eeg[:,:],Fs=eeg.Fs,border=border)
else:
for i in range(0,out.num_datapoints,100000):
out[i:min(i+100000,out.num_datapoints),:] = filtfilt_low(fl,eeg[i:min(i+100000,out.num_datapoints),:],Fs=eeg.Fs,border=border)
elif btype == "hp":
#for i_c in range(eeg.num_channels):
if not windowed:
out[:,:] = filtfilt_high(fh,eeg[:,:],Fs=eeg.Fs,border=border)
else:
for i in range(0,out.num_datapoints,100000):
out[i:min(i+100000,out.num_datapoints),:] = filtfilt_high(fh,eeg[i:min(i+100000,out.num_datapoints),:],Fs=eeg.Fs,border=border)
elif btype == "bp":
if not windowed:
out[:,:] = filtfilt_band(fl,fh,eeg[:,:],Fs=eeg.Fs,border=border)
else:
for i in range(0,out.num_datapoints,100000):
out[i:min(i+100000,out.num_datapoints),:] = filtfilt_band(fl,fh,eeg[i:min(i+100000,out.num_datapoints),:],Fs=eeg.Fs,border=border)
elif btype == "bs":
if not windowed:
out[:,:] = filtfilt_bandstop(fl,fh,eeg[:,:],Fs=eeg.Fs,border=border)
else:
for i in range(0,out.num_datapoints,100000):
out[i:min(i+100000,out.num_datapoints),:] = filtfilt_bandstop(fl,fh,eeg[i:min(i+100000,out.num_datapoints),:],Fs=eeg.Fs,border=border)
def filter(self):
"Do the actual filtering"
#Slice-alignment
self.check_before_filtering()
self.Ya = upsample(self._data[:,self.ch_for_slice_alignment],self.upsf)
self.realign_triggers()
for i in range(self._data.shape[1]):
self.Ya = upsample(self._data[:,i],self.upsf)
#ff.Yah = N.memmap("tmp/Yah.np",mode="w+",dtype=N.float64,shape=ff.Ya.shape)
self.Yah = filtfilt_high(1.0,self.Ya,Fs=10000.0)
self.subtract_mean_from_Ya2(n_ma=20)
#ar1 = ff.Ya2.copy()
self.subtract_residual_obs()
def filter(self):
"Do the actual filtering"
#Slice-alignment
self.check_before_filtering()
self.Ya = upsample(self._data[:, self.ch_for_slice_alignment],
self.upsf)
self.realign_triggers()
for i in range(self._data.shape[1]):
self.Ya = upsample(self._data[:, i], self.upsf)
#ff.Yah = N.memmap("tmp/Yah.np",mode="w+",dtype=N.float64,shape=ff.Ya.shape)
self.Yah = filtfilt_high(1.0, self.Ya, Fs=10000.0)
self.subtract_mean_from_Ya2(n_ma=20)
#ar1 = ff.Ya2.copy()
self.subtract_residual_obs()
def remove_artifacts(self):
"""Tries to remove the artifacts.
Uses mixture of mean-subtraction and OBS-PCA-Subtraction
Needs self.slcs to be set.
Saves example data in self.examples to make evaluation of filter quality possible"""
def obs_fit_error(p,y,x):
err = n.zeros((x.shape[0]),"d")
err += p[0]
for i in range(self.obs_size):
err += y[:,i]*p[i+1]
err -= x
return abs(err**2).sum()
def obs_fit_error_lsq(p,y,x):
err = n.zeros((x.shape[0]),"d")
err += p[0]
for i in range(self.obs_size):
err += y[:,i]*p[i+1]
err -= x
return err
def las_fit_error_lsq(p,y,x):
"""Local artifact subtraction"""
err = p[0]*y
err -= x
return err
#Shortnames
eeg = self.eeg
slcs = self.slcs_ups
sw = self._new_slice_width *10
k = self.num_neighbors
self.examples=[]
num_examples=10
#Loop over channels
if show_progressbar:
pbar = ProgressBar().start()
for i_ch in range(eeg.num_channels):
#Make Highpass-Version of channel
#ch = eeg[:,i_ch].copy()
y , fn = upsample_to_memmap(eeg[:,i_ch],10)
y_hp,fn_hp = tmp_memmap(dtype=y.dtype,shape=y.shape,mode="w+")
y_hp[:] = filtfilt_high(1, y, Fs=10000.0)
y_out,fn_out = tmp_memmap(dtype=y.dtype,shape=y.shape,mode="w+")
y_out[:] = y[:]
#ch_hp = filtfilt_high(1.0,ch,Fs=eeg.Fs)
neighbors = n.zeros((sw+2*self._pa_zeros,k))
#Prefill
for i in range(k):
#print neighbors[:,i].shape, eeg[slcs[i]-sw/2:slcs[i]+sw/2,i_ch].shape
neighbors[:,i] = prepend_append_zeros( y_hp[slcs[i]-sw/2:slcs[i]+sw/2] , self._pa_zeros)
#Loop over slices and filter
next_subst = 0
for i,t in enumerate(slcs):
try:
if i>k/2 and i<len(slcs)-k/2:
neighbors[:,next_subst] = prepend_append_zeros( y_hp[slcs[i+k/2]-sw/2:slcs[i+k/2]+sw/2] , self._pa_zeros)
next_subst+=1
next_subst=next_subst%k
tmp = prepend_append_zeros( y[t-sw/2:t+sw/2] , self._pa_zeros)
#Subtraction
#Shift/scale template
template = neighbors.mean(axis=1)
p = [1.0, 0]
p_las = leastsq(las_fit_error_lsq,p,args=(template,tmp))[0]
#print p_las[0],
tmp -= p_las[0]*template
#Beispieldaten speichern: Teil 1
if i_ch == self.ch_for_slice_alignment:
if i%(len(slcs)/num_examples)==(len(slcs)/num_examples)/2:
print "examples, Teil 1"
example = {}
example["raw"] = prepend_append_zeros(y[t-sw/2:t+sw/2].copy() , self._pa_zeros )
example["mean"] = p_las[0]*template
#OBS-Fit
components = pcafilt.unmix(neighbors) #OBS will be first 5 components
components -= components.mean(axis=0).reshape(1,-1).repeat(components.shape[0],0) #demeaning column-wise
obs = components[:,:self.obs_size].copy()
#Fit OBS to artifact
p = [0]+[0]*self.obs_size
#p_lsq = fmin(obs_fit_error,p,args=(obs,eeg[t-sw/2:t+sw/2,i_ch]),maxiter=1e5,maxfun=1e5)#[0]
p_lsq = leastsq(obs_fit_error_lsq,p,args=(obs,tmp))[0]
#print i,t,"p_lsq", p_lsq
fit = n.zeros((obs.shape[0]),"d")
fit +=p_lsq[0]
for j in range(self.obs_size):
fit += obs[:,j]*p_lsq[j+1]
tmp -= fit
try:
#eeg[t/10-sw/10/2:t/10+sw/10/2,i_ch] = tmp[self._pa_zeros:-self._pa_zeros][::10]
y_out[t-sw/2:t+sw/2] = tmp[self._pa_zeros:-self._pa_zeros][:]
except ValueError, ve:
print i_ch, i,t, eeg[t/10-sw/10/2:t/10+sw/10/2,i_ch].shape, tmp[self._pa_zeros:-self._pa_zeros][::10].shape
#Beispieldaten speichern: Teil 2
if i_ch == self.ch_for_slice_alignment:
if i%(len(slcs)/num_examples)==(len(slcs)/num_examples)/2:
print "examples, Teil 2"
#example["fit"] = n.zeros(example["raw"].shape) #fit.copy()
example["fit"] = fit.copy()
example["obs"] = obs.copy()
example["filt1"] = (tmp + fit).copy()
example["filt2"] = tmp.copy()
self.examples.append(example)
if show_progressbar:
pbar.update((i_ch+i/len(slcs))*100/eeg.num_channels)
except Exception, e:
print "Error occurred at slice at t=",t,", ignored"
print e
def freqfilt_eeg(fn_in,
fn_out,
btype="lp",
fl=None,
fh=None,
border=3,
windowed=False):
eeg = eegpy.open_eeg(fn_in)
out = eegpy.F32(fn_out,
"w+",
shape=eeg.shape,
cNames=eeg.channel_names,
Fs=eeg.Fs)
if btype == "lp":
if not windowed:
out[:, :] = filtfilt_low(fl, eeg[:, :], Fs=eeg.Fs, border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_low(
fl,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "hp":
#for i_c in range(eeg.num_channels):
if not windowed:
out[:, :] = filtfilt_high(fh, eeg[:, :], Fs=eeg.Fs, border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_high(
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "bp":
if not windowed:
out[:, :] = filtfilt_band(fl,
fh,
eeg[:, :],
Fs=eeg.Fs,
border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_band(
fl,
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "bs":
if not windowed:
out[:, :] = filtfilt_bandstop(fl,
fh,
eeg[:, :],
Fs=eeg.Fs,
border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i +
100000, out.num_datapoints), :] = filtfilt_bandstop(
fl,
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
def remove_artifacts(self):
"""Tries to remove the artifacts.
Uses mixture of mean-subtraction and OBS-PCA-Subtraction
Needs self.slcs to be set.
Saves example data in self.examples to make evaluation of filter quality possible"""
def obs_fit_error(p, y, x):
err = n.zeros((x.shape[0]), "d")
err += p[0]
for i in range(self.obs_size):
err += y[:, i] * p[i + 1]
err -= x
return abs(err**2).sum()
def obs_fit_error_lsq(p, y, x):
err = n.zeros((x.shape[0]), "d")
err += p[0]
for i in range(self.obs_size):
err += y[:, i] * p[i + 1]
err -= x
return err
def las_fit_error_lsq(p, y, x):
"""Local artifact subtraction"""
err = p[0] * y
err -= x
return err
#Shortnames
eeg = self.eeg
slcs = self.slcs_ups
sw = self._new_slice_width * 10
k = self.num_neighbors
self.examples = []
num_examples = 10
#Loop over channels
if show_progressbar:
pbar = ProgressBar().start()
for i_ch in range(eeg.num_channels):
#Make Highpass-Version of channel
#ch = eeg[:,i_ch].copy()
y, fn = upsample_to_memmap(eeg[:, i_ch], 10)
y_hp, fn_hp = tmp_memmap(dtype=y.dtype, shape=y.shape, mode="w+")
y_hp[:] = filtfilt_high(1, y, Fs=10000.0)
y_out, fn_out = tmp_memmap(dtype=y.dtype, shape=y.shape, mode="w+")
y_out[:] = y[:]
#ch_hp = filtfilt_high(1.0,ch,Fs=eeg.Fs)
neighbors = n.zeros((sw + 2 * self._pa_zeros, k))
#Prefill
for i in range(k):
#print neighbors[:,i].shape, eeg[slcs[i]-sw/2:slcs[i]+sw/2,i_ch].shape
neighbors[:, i] = prepend_append_zeros(
y_hp[slcs[i] - sw / 2:slcs[i] + sw / 2], self._pa_zeros)
#Loop over slices and filter
next_subst = 0
for i, t in enumerate(slcs):
try:
if i > k / 2 and i < len(slcs) - k / 2:
neighbors[:, next_subst] = prepend_append_zeros(
y_hp[slcs[i + k / 2] - sw / 2:slcs[i + k / 2] +
sw / 2], self._pa_zeros)
next_subst += 1
next_subst = next_subst % k
tmp = prepend_append_zeros(y[t - sw / 2:t + sw / 2],
self._pa_zeros)
#Subtraction
#Shift/scale template
template = neighbors.mean(axis=1)
p = [1.0, 0]
p_las = leastsq(las_fit_error_lsq, p,
args=(template, tmp))[0]
#print p_las[0],
tmp -= p_las[0] * template
#Beispieldaten speichern: Teil 1
if i_ch == self.ch_for_slice_alignment:
if i % (len(slcs) / num_examples) == (
len(slcs) / num_examples) / 2:
print "examples, Teil 1"
example = {}
example["raw"] = prepend_append_zeros(
y[t - sw / 2:t + sw / 2].copy(),
self._pa_zeros)
example["mean"] = p_las[0] * template
#OBS-Fit
components = pcafilt.unmix(
neighbors) #OBS will be first 5 components
components -= components.mean(axis=0).reshape(
1, -1).repeat(components.shape[0],
0) #demeaning column-wise
obs = components[:, :self.obs_size].copy()
#Fit OBS to artifact
p = [0] + [0] * self.obs_size
#p_lsq = fmin(obs_fit_error,p,args=(obs,eeg[t-sw/2:t+sw/2,i_ch]),maxiter=1e5,maxfun=1e5)#[0]
p_lsq = leastsq(obs_fit_error_lsq, p, args=(obs, tmp))[0]
#print i,t,"p_lsq", p_lsq
fit = n.zeros((obs.shape[0]), "d")
fit += p_lsq[0]
for j in range(self.obs_size):
fit += obs[:, j] * p_lsq[j + 1]
tmp -= fit
try:
#eeg[t/10-sw/10/2:t/10+sw/10/2,i_ch] = tmp[self._pa_zeros:-self._pa_zeros][::10]
y_out[t - sw / 2:t +
sw / 2] = tmp[self._pa_zeros:-self._pa_zeros][:]
except ValueError, ve:
print i_ch, i, t, eeg[
t / 10 - sw / 10 / 2:t / 10 + sw / 10 / 2,
i_ch].shape, tmp[self._pa_zeros:-self.
_pa_zeros][::10].shape
#Beispieldaten speichern: Teil 2
if i_ch == self.ch_for_slice_alignment:
if i % (len(slcs) / num_examples) == (
len(slcs) / num_examples) / 2:
print "examples, Teil 2"
#example["fit"] = n.zeros(example["raw"].shape) #fit.copy()
example["fit"] = fit.copy()
example["obs"] = obs.copy()
example["filt1"] = (tmp + fit).copy()
example["filt2"] = tmp.copy()
self.examples.append(example)
if show_progressbar:
pbar.update(
(i_ch + i / len(slcs)) * 100 / eeg.num_channels)
except Exception, e:
print "Error occurred at slice at t=", t, ", ignored"
print e
