code: def return_hdr(self):
          
          subj_id = ''  # no subject information in the header
  
          hdr = _parse_ini(self.filename)
  
          self.eeg_file = self.filename.parent / hdr['Common Infos']['DataFile']
          self.vmrk_file = self.filename.parent / hdr['Common Infos']['MarkerFile']
          self.mrk = _parse_ini(self.vmrk_file)
  
          start_time = _read_datetime(self.mrk)
          self.s_freq = 1e6 / float(hdr['Common Infos']['SamplingInterval'])
          chan_name = [v[0] for v in hdr['Channel Infos'].values()]
          self.gain = array([float(v[2]) for v in hdr['Channel Infos'].values()])
  
          # number of samples
          self.data_type = BV_DATATYPE[hdr['Binary Infos']['BinaryFormat']]
          N_BYTES = dtype(self.data_type).itemsize
          n_samples = int(self.eeg_file.stat().st_size / N_BYTES / len(chan_name))
  
          self.dshape = len(chan_name), int(n_samples)
          self.data_order = BV_ORIENTATION[hdr['Common Infos']['DataOrientation']]
  
          orig = {
              'vhdr': hdr,
              'vmrk': self.mrk,
              }
  
          return subj_id, start_time, self.s_freq, chan_name, n_samples, orig
docs: Return the header for further use.
  
          Returns
          -------
          subj_id : str
              subject identification code
          start_time : datetime
              start time of the dataset
          s_freq : float
              sampling frequency
          chan_name : list of str
              list of all the channels
          n_samples : int
              number of samples in the dataset
          orig : dict
              additional information taken directly from the header