prep

A module for io and preprocessing

cmgraph.prep.eeg_emg_alignment(eeg_fName, emg_df, sfreq_final, emg_freq, report_fName=None, start_marker=True, fir=[1, None], PREP=True, montage='standard_1020')[source]

This function takes .set format for eeg and txt format for emg.

Parameters:
eeg_fName : string

eeg_fName should be in .set form

emg_fName : string

emg_fName should be in .txt form

report_fName: string

optional, default to None which will not generate a report for the reprocessing result. When reproty_fName is specified, two reports will be generated at the suggested directory including one in HTML, and one in .h5 format. The .h5 format is an edittable report.

montage: string

The defaut montage is set to standard 1020 montage

start_marker : boolean

if start_marker is true, the segment before the first marker will be cropped, defaults to True

fir: list

the lower and upper boundary of filter. If the boundary is set to None, then the filter become high-pass or low-pass filter.

PREP: boolean

the EEG preprocessing pipeline process, defauts to True. It can de deactivated when set to false.

emg_chs_selected : list of int

the index of emg channels (columns) that are supposed to be used in further analysis, defaults to ‘all’

Returns:
mne raw object containing aligned eeg and emg data

Notes

Make sure the eeg recording are no less than the emg recording. The current version of this function crop emg signal with respect to emg in order to keep these data aligned.

Examples

>>>eeg_fName = r’D:DataRuiJinFirstStroke11JanEEGsubj1_healthy_session1.set’ >>>emg_fName = r’D:DataRuiJinFirstStroke11JanEMGsubj1_healthy_session1.txt’ >>>emg_df = pd.read_csv(emg_fName, header = None, skiprows=3,

sep = ‘ ‘,engine = ‘python’)

>>>eeg_emg_alignment(eeg_fName,emg_df,emg_freq=1000,sfreq_final=500,report_fName=None,PREP=False)

cmgraph.prep.emg_io(emg_fName, skiprows, sep=' ', emg_chs_selected='all')[source]

this function takes .txt format emg file and renders to a pandas dataframe for further processing.

Parameters:
emg_fName : string

emg_fName should be in .txt or in .csv form.

emg_chs_selected : list of int

the index of emg channels (columns) that are supposed to be used in further analysis, defaults to ‘all’

sep : string

the seperator to be specified when reading the txt file with pandas.read_csv

skiprows : int

rows to be skipped. This applies for data containing emg information at the begining of the data file.

Returns:
pandas’s dataframe

Notes

The .txt file or .csv file should not have headers. If so, please use skiprow to trim them. The first column should be 0 in the emg_chs_selected parameter

Examples

>>>emg_fName = r’D:DataRuiJinFirstStroke11JanEMGsubj1_healthy_session1.txt’ >>>emg_io(emg_fName, skiprows = 3, emg_chs_selected=’all’)

cmgraph.prep.epochs_basedon_emg(raw_hybrid, ref_emg, windowLen, step=100, threshold=0.5, report_fName=None, add_to_existed_report=False, reject_criteria={'eeg': 0.0003, 'emg': 10000000000.0}, flat_criteria={'eeg': 5e-07}, tmin=0.0, tmax=3.0, save_fName=None)[source]

this function takes .set format for eeg and txt format for emg.

Parameters:
raw_hybrid: mne raw object

The raw object containing aligned eeg and emg

ref_emg: list of string

the emg channels that the algorithm refers to. They should be channels in input raw_hybrid. The algorithm will consider ‘the length of the string’ as number of movements.

report_fName: string

optional, default to None which will not generate a report for the epoching result. When reproty_fName is specified, two reports will be generated at the suggested directory including one in HTML, and one in .h5 format. The .h5 format is an edittable report.

add_to_existed_report: boolean

It is supposed to be true when one wants to add the epoching report into an existed report.

windowLen: int

rough duration estimation of the movement

step: int

equivalent to resolution, defaults to 100

threshold: float

the threshold should be in [0,1], represnting the quantile of the energy for all windows

reject_criteria: dict

epochs containing eeg and emg that exceed rejection criteria will be rejected. defaults to dict(eeg=30e-5,emg=1e10)

flat_criteria: dict

epochs containing eeg and emg whose amplitude are lower than flat criteria will be rejected. Defaults to dict(eeg=1e-6). If reject_criteria and flat_criteria are both set to None, then no epochs rejection would take place

tmin: float

the begining of the epochs with respect to the movement onsets

tmax: float

the end of the epochs with respect to the movement onsets

savefName: string

the path and filename of the saving epochs. It defaults to None, that is the epochs would not be saved

Returns:
mne epochs
cmgraph.prep.firstOnsetD(possibleOnsets)[source]

an auxilary function that identify true movement onsets for all the possible onsets identified based on energy threshold.

Parameters:
possibleOnsets: list

list of all the possible onsets

Returns:
true movement onsets