fname = '/home/jrking/Desktop/R0174_block1_1.13.17.sqd'
from mne.io.pick import channel_type

raw = read_raw_kit(fname)
stim_channel = [ii for ii in range(len(raw.info['chs']))
                if channel_type(raw.info, ii) == 'stim'][0]
raw.load_data()

def fix_trigger_channel(data, shortest_event=3):
    diff = np.diff(data)  # isolate onset and offsets by taking derivative
    starts = np.where(diff > 0)[0]  # keep onsets indices only
    good = np.where(np.diff(starts) >= shortest_event)[0]
    new_data = np.zeros_like(data)  # create a new trigger channels

    # for each good start, fill the array with 50 times the actual trigger value
    for t in range(shortest_event):
        new_data[starts[good] + t] = data[starts[good] + shortest_event]

    return new_data

# Clean raw trigger channel to ensure that the values are either = trigger or 0 (no transition < 1 time sample)
raw._data[stim_channel] = fix_trigger_channel(255 - raw._data[stim_channel])
events = find_events(raw, stim_channel='STI 014', shortest_event=1)

# only keep the relevant events
good = np.where(events[:, 2] <= 27)[0]
events = events[good]

# somehow, only every other event seem to be important
events = events[::2]