code: 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
  
imports: import numpy
  from ...io import BaseRaw
  from ...utils import _validate_type, verbose
  from ..nirs import _validate_nirs_info
label_desc: Use image node_id 1 for calling a global function with example usage: scalp_coupling_index(raw, l_freq, h_freq, l_trans_bandwidth, h_trans_bandwidth, verbose) and returns: sci