Checking Bad Channels

check_bad_chs

    def check_bad_chs(self, threshold=0.05, reject=3e-12, n_chan=5):
        """
        Check for flat-line channels or channels that repeatedly exceeded
        threshold.
        """
        ds = self.load_events(drop_bad_chs=False)
        ds = ds[ds['experiment'] == 'fixation']
        threshold = ds.n_cases * threshold
        epochs = E.load.fiff.mne_epochs(ds, tmin=-.2, tmax=.6,
                                        drop_bad_chs=False, verbose=False,
                                        baseline=(None, 0), preload=True,
                                        reject={'mag': reject})
        if epochs.drop_log:
            bads = E.Factor(sum(epochs.drop_log, []))
            bads = E.table.frequencies(bads)
            bads = bads[bads['n'] > threshold]['cell'].as_labels()
        else:
            bads = []
        picks = mne.fiff.pick_types(epochs.info, exclude=[])
        data = epochs.get_data()[:, picks, :]
        flats = []
        diffs = np.diff(data) == 0
        for epoch in diffs:
            # channels flat > 50% time period
            flats.append(np.where(np.mean(epoch, 1) >= .5)[0])
        flats = np.unique(np.hstack(flats))
        flats = ['MEG %03d' % (x + 1) for x in flats]

        bad_chs = np.unique(np.hstack((bads, flats)).ravel())
        if len(bad_chs) > n_chan:
            drop = 1
        else:
            drop = 0

        with open(self.get('bads-file'), 'w') as FILE:
            import datetime
            date = datetime.datetime.now().ctime()
            FILE.write('# Log of bad channels for %s written on %s\n\n'
                       % (self.get('subject'), date))
            FILE.write('bads=%s\n' % bad_chs)
            FILE.write('drop=%s' % drop)
        return bad_chs