def load_eeg_dataset(path, filename, attrib, TR, eliminated_vols=None, **kwargs):
"""
**kwargs:
- type:
* 'psd': Power Spectrum Density using matplotlib.specgram
additional parameters to be included NFFT and noverlap
* 'fft': Power Spectrum and Phase using scipy.fft
* 'time': EEG timecourse in every TR.
"""
type = "time"
for arg in kwargs:
if arg == "type":
type = kwargs[arg]
print "type = " + type
# load eeg data
[data, eeg_info] = load_eeg_data(path, filename, TR, eliminatedVols=eliminated_vols)
channel_ids = eeg_info["channel_ids"]
dt = eeg_info["dt"]
kwargs["dt"] = dt
if (type == "psd") or (type == "fft"):
[samples, freq] = spectrum_eeg(data, **kwargs)
data = samples.reshape(samples.shape[0], samples.shape[1], -1)
# mvpa analysis: attributes and dataset
attr = SampleAttributes(attrib)
print "Building dataset..."
ds = Dataset.from_channeltimeseries(data, channelids=channel_ids, targets=attr.targets, chunks=attr.chunks)
if (type == "psd") or (type == "fft"):
ds.a["frequiencies"] = freq
ds.a["shape"] = samples.shape
ds.a["timepoints"] = np.arange(0, TR, dt)
del data
if "samples" in locals():
del samples
return ds
for j in channelSelected:
start = markers[i][1] + (onSet / dt)
stop = start + (nSamples / dt)
nVec = np.array(data.T[j][start:stop])
if nVec.shape[0] != (nSamples / dt):
nVec.resize(nSamples / dt)
print "yes"
dataRes[j][i] = nVec
# Select data based on channels Selected
dataResSel = np.take(dataRes, channelSelected, axis=0)
ch_infoSel = np.take(ch_info, channelSelected)
# reshape data to feed Dataset
dataResSel = np.reshape(dataResSel, (nTrials, -1, nSamples / dt))
# Eliminate first n. runs
volToEliminate = 5
dataResSel = dataResSel[: nTrials - volToEliminate]
# mvpa analysis: attributes and dataset
attr = SampleAttributes("/home/robbis/fmri_datasets/monks/monks_attrib_pymvpa.txt")
ds = Dataset.from_channeltimeseries(
dataResSel, t0=0, dt=dt, channelids=ch_infoSel, targets=attr.targets, chunks=attr.chunks
)
del dataRes, dataResSel