Filtering Time Series

Filtering is a type of operation that takes as an input one time series and outputs another. For example Butterworth band-pass filter may remove unwanted frequencies from your signal. Computing Wavelets is also a form of filtering operation that takes one time series and outputs another one with signal that is decomposed into wavelet components


All filter objects define function filter. This is the function you call to make filter do its job

Let us start with something simple - MonopolarToBipolarMapper.


This filter takes as inputs an array of monopolar eeg data - timeseries parameter below and the array of bipolar pairs (bipolar_pairs) and outputs another time series containing pairwise differences for electrode pairs specified in bipolar_pairs

Here is the syntax:

from import MonopolarToBipolarMapper
m2b = MonopolarToBipolarMapper(bipolar_pairs=bipolar_pairs)
bp_eegs = m2b.filter(timeseries=base_eegs)

We import MonopolarToBipolarMapper from PTSA package , crteate an instance of MonopolarToBipolarMapper with appropriate parameters and then call filter function to compute pairwise signal differences. Here is the output:

<xray.TimeSeries (bipolar_pairs: 141, events: 1020, time: 1800)>
array([[[  7119.14164 ,   7119.673316,   7119.14164 , ...,   7156.35896 ,
           7159.549016,   7164.3341  ],
        [  7175.499296,   7178.157676,   7186.132816, ...,   7022.376608,
           7009.084708,   7009.084708],
        [  7061.188956,   7063.31566 ,   7067.037392, ...,   7227.071868,
           7228.13522 ,   7221.223432],


Notice that this TimeSeries object is indexed by bipolar_pairs. As a matter of fact if you type:


you will get

<xray.DataArray 'bipolar_pairs' (bipolar_pairs: 141)>
array([('001', '002'), ('001', '009'), ('002', '003'), ('002', '010'),
       ('003', '004'), ('003', '011'), ('004', '005'), ('004', '012'),


To use Butterworth filtering inside PTSA you have two choices: use ButterworthFilter object and passing TimeSeries object to its .filter method or use a convenience function inside TimeSeries object.

Let’s us start by showing first ButterworthFilter:

from import ButterworthFilter
b_filter = ButterworthFilter(freq_range=[58., 62.], filt_type='stop', order=4)
bp_eegs_filtered = b_filter.filter(timeseries=bp_eegs)

Here we create ButterworthFilter object (after importing it from PTSA’s filters package) and specify filter parameters: we specify frequency range that we want to filter out (to remove frequencies we set filt_type to 'stop') and specify filter order (here it is 4)

As before, once the filter object is initialized we call filter function to get the result (filtered TimeSeries).

If you prefer you may use alternative way of running Butterworth filter on a TimeSeries by calling filtered function on a Timeseries object

bp_eegs_filtered_1 = bp_eegs.filtered(freq_range=[58., 62.], filt_type='stop', order=4)