Source code for mne_nirs.preprocessing._scalp_coupling_segmented

# Authors: Robert Luke <mail@robertluke.net>
#
# License: BSD (3-clause)

import numpy as np
from mne.filter import filter_data
from mne.io import BaseRaw
from mne.preprocessing.nirs import _validate_nirs_info
from mne.utils import _validate_type, verbose


[docs] @verbose def scalp_coupling_index_windowed( raw, time_window=10, threshold=0.1, l_freq=0.7, h_freq=1.5, l_trans_bandwidth=0.3, h_trans_bandwidth=0.3, verbose=False, ): """ Compute scalp coupling index for each channel and time window. As described in [1]_ and [2]_. This method provides a metric of data quality along the duration of the measurement. The user can specify the window over which the metric is computed. Parameters ---------- raw : instance of Raw The haemoglobin data. time_window : number The duration of the window over which to calculate the metric. Default is 10 seconds as in PHOEBE paper. threshold : number Values below this are marked as bad and annotated in the raw file. %(l_freq)s %(h_freq)s %(l_trans_bandwidth)s %(h_trans_bandwidth)s %(verbose)s Returns ------- raw : instance of Raw The Raw data. Optionally annotated with bad segments. scores : array (n_nirs, n_windows) Array of peak power values. times : list List of the start and end times of each window used to compute the peak spectral power. References ---------- .. [1] Pollonini L et al., “PHOEBE: a method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy” in Biomed. Opt. Express 7, 5104-5119 (2016). .. [2] Hernandez, Samuel Montero, and Luca Pollonini. "NIRSplot: a tool for quality assessment of fNIRS scans." Optics and the Brain. Optical Society of America, 2020. """ raw = raw.copy().load_data() _validate_type(raw, BaseRaw, "raw") picks = _validate_nirs_info(raw.info, fnirs="od", which="Scalp coupling index") filtered_data = filter_data( raw._data, raw.info["sfreq"], l_freq, h_freq, picks=picks, verbose=verbose, l_trans_bandwidth=l_trans_bandwidth, h_trans_bandwidth=h_trans_bandwidth, ) window_samples = int(np.ceil(time_window * raw.info["sfreq"])) n_windows = int(np.floor(len(raw) / window_samples)) scores = np.zeros((len(picks), n_windows)) times = [] for window in range(n_windows): start_sample = int(window * window_samples) end_sample = start_sample + window_samples end_sample = np.min([end_sample, len(raw) - 1]) t_start = raw.times[start_sample] t_stop = raw.times[end_sample] times.append((t_start, t_stop)) for ii in range(0, len(picks), 2): c1 = filtered_data[picks[ii]][start_sample:end_sample] c2 = filtered_data[picks[ii + 1]][start_sample:end_sample] c = np.corrcoef(c1, c2)[0][1] scores[ii, window] = c scores[ii + 1, window] = c if (threshold is not None) & (c < threshold): raw.annotations.append( t_start, time_window, "BAD_SCI", ch_names=[raw.ch_names[ii : ii + 2]], ) scores = scores[np.argsort(picks)] return raw, scores, times