A class used to remove artifacts from EEG signals, the default EEG signal has a duration of 6 seconds and 19 channels.
EEG data with artifacts to be filtered, and EEG data in resting state, dimensions N x channels where N is the length of an EEG record
_wpt_filter: filters data selecting the most corrupt node in a wpt removes it and makes the reconstruction of the signal, a wpt is computed per channel
wptemd_filter: applies _wpt_filter, and after decomposing each filtered channel into imfs using emd, to filter again the signal removing the most corrupt imf computing J a criterion based on entropy and std normalized with respect to the resting data
wptica_filter: like in wptemd_filter applies _wpt_filter to filter the data, afterward a second ICA based filter is applied where is removed the most contaminated ICA component, and reconstructed the sources
Data_filtered a filtered version of the original EEG data
importnumpyasnpfromPywptemd.wptemdimportwptemdfs=500data=np.random.randn(19,6*fs)data_rest=np.random.randn(19,6*fs)*.25parameters= {'wavelet':'dmey','maxlevel':7,# wavelet parameters'imf_opts':{'stop_method':'rilling','rilling_thresh':(0.05,0.5,0.05)},# emd parameters'w':0.5,# emd selection criterion J parameter'n_components':19}# ICA parameterfilter=wptemd(**parameters)wpt_filtered=filter._wpt_filter(data)print(wpt_filtered.shape)data_filtered_wptemd=filter.wptemd_filter(data,data_rest)print(data_filtered_wptemd.shape)data_filtered_wtpica=filter.wptica_filter(data)print(data_filtered_wtpica.shape)[1] Bono, V., Das, S., Jamal, W., & Maharatna, K. (2016). Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG. Journal of neuroscience methods, 267, 89-107.
