wonambi.trans.montage module

wonambi.trans.montage.compute_average_regress(x, idx_chan)

Take the mean across channels and regress out the mean from each channel

Parameters:

• x (ndarray) – 2d array with channels on one dimension

• idx_chan – which axis contains channels

Returns:

ndarray – same as x, but with the mean being regressed out

wonambi.trans.montage.create_bipolar_chan(chan, max_dist)

wonambi.trans.montage.create_virtual_channel(data, new_chan_name='virtual', method='average')

Create a virtual channel by averaging several channels.

Parameters:

• data (instance of DataRaw) – the data to filter

• new_chan_name (str) – label for the virtual channel

• method (str) – ‘average’

Returns:

mdata (instance of Data) – virtual data

wonambi.trans.montage.montage(data, ref_chan=None, ref_to_avg=False, bipolar=None, method='average')

Apply linear transformation to the channels.

Parameters:

• data (instance of DataRaw) – the data to filter

• ref_chan (list of str) – list of channels used as reference

• ref_to_avg (bool) – if re-reference to average or not

• bipolar (float) – distance in mm to consider two channels as neighbors and then compute the bipolar montage between them.

• method (str) – ‘average’ or ‘median’ or ‘regression’. ‘average’ / ‘median’ takes the mean / median across the channels selected as reference (it can be all) and subtract it from each channel. ‘regression’ keeps the residuals after regressing out the mean across channels.

Returns:

filtered_data (instance of DataRaw) – filtered data

Notes

If you don’t change anything, it returns the same instance of data.