1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
|
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from ...io import BaseRaw
from ...utils import _validate_type, verbose
from ..nirs import _validate_nirs_info
@verbose
def scalp_coupling_index(
raw,
l_freq=0.7,
h_freq=1.5,
l_trans_bandwidth=0.3,
h_trans_bandwidth=0.3,
verbose=False,
):
r"""Calculate scalp coupling index.
This function calculates the scalp coupling index
:footcite:`pollonini2014auditory`. This is a measure of the quality of the
connection between the optode and the scalp.
Parameters
----------
raw : instance of Raw
The raw data.
%(l_freq)s
%(h_freq)s
%(l_trans_bandwidth)s
%(h_trans_bandwidth)s
%(verbose)s
Returns
-------
sci : array of float
Array containing scalp coupling index for each channel.
References
----------
.. footbibliography::
"""
_validate_type(raw, BaseRaw, "raw")
picks = _validate_nirs_info(raw.info, fnirs="od", which="Scalp coupling index")
raw = raw.copy().pick(picks).load_data()
zero_mask = np.std(raw._data, axis=-1) == 0
filtered_data = raw.filter(
l_freq,
h_freq,
l_trans_bandwidth=l_trans_bandwidth,
h_trans_bandwidth=h_trans_bandwidth,
verbose=verbose,
).get_data()
sci = np.zeros(picks.shape)
for ii in range(0, len(picks), 2):
with np.errstate(invalid="ignore"):
c = np.corrcoef(filtered_data[ii], filtered_data[ii + 1])[0][1]
if not np.isfinite(c): # someone had std=0
c = 0
sci[ii] = c
sci[ii + 1] = c
sci[zero_mask] = 0
sci = sci[np.argsort(picks)] # restore original order
return sci
|
