tnbsclean 1.20

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tnbsclean

Description

tnbsclean is a Python package for preprocessing neurophysiological data.
It helps clean EEG, MEG, EMG, and ECG signals in MNE-Python by removing stimulation artifacts, and it supports converting Blackrock .nsx recordings into MNE Raw objects for seamless analysis.

Installation

You can install the package via pip from PyPI:

pip install tnbsclean

Example use for data cleaning from mne raw object

import tnbsclean as tnbs

# Example parameters
raw  # Raw time series data in MNE's raw object format
half_win = 3  # Half window size around each stimulus (in samples) that will be chopped away from the artifact peak point
threshold = 2  # Threshold constant above which stimulus is considered significant

# Apply stimulus cleaning 
raw_cleaned, spike_idxs = tnbs.get_stim_markers(raw, half_win, threshold) #cleans based on ECG, returns spike indices

Example use for converting nsx files mne raw object

# Example parameters for reading Blackrock data
folder        = r'/path/to/data'
aux_chann     = ['L_EMG1', 'R_EMG1', 'ECG']
chan_type_map = {'L_EMG1': 'emg', 'R_EMG1': 'emg', 'ECG': 'ecg'}
file_type     = 'hub'
extension     = 'ns3'

# Load raw data
raw = tnbs.read_blackrock(folder, aux_chann, chan_type_map, file_type, extension)