def present(eeg: EEG = None,
save_fn=None,
stim_types=None,
itis=None,
additional_labels={},
secs=0.07,
volume=0.8,
tone1_hz=440,
tone2_hz=528,
do_fixation=True):
soa = 0 # ?
# additional_labels is dict with column names as keys and column vecs as values,
# that will be added to the dataframe
# def present(duration=120, n_trials=10, iti=0.3, soa=0.2, jitter=0.2,
# secs=0.2, volume=0.8, random_state=None):
# Create markers stream outlet
# info = StreamInfo('Markers', 'Markers', 1, 0, 'int32', 'myuidw43536')
# info = StreamInfo('Markers', 'Markers', 1 + len(additional_labels), 0, 'int32', 'myuidw43536')
info = StreamInfo("Markers", "Markers", 1 + len(additional_labels), 0,
"float32", "myuidw43536")
outlet = StreamOutlet(info)
# np.random.seed(random_state)
markernames = [1, 2]
start = time.time()
# Initialize stimuli
# aud1 = sound.Sound('C', octave=5, sampleRate=44100, secs=secs)
aud1 = sound.Sound(tone1_hz,
secs=secs) # , octave=5, sampleRate=44100, secs=secs)
aud1.setVolume(volume)
# aud2 = sound.Sound('D', octave=6, sampleRate=44100, secs=secs)
aud2 = sound.Sound(tone2_hz, secs=secs)
aud2.setVolume(volume)
auds = [aud1, aud2]
# Setup trial list
trials = DataFrame(dict(sound_ind=stim_types, iti=itis))
record_duration_int = int(round(trials.iti.values.sum(), -1))
record_duration_float = np.float32(record_duration_int)
for col_name, col_vec in additional_labels.items():
trials[col_name] = col_vec
if do_fixation:
# Setup graphics
mywin = visual.Window([1920, 1080],
monitor="testMonitor",
units="deg",
fullscr=True)
fixation = visual.GratingStim(win=mywin,
size=0.2,
pos=[0, 0],
sf=0,
rgb=[1, 0, 0])
fixation.setAutoDraw(True)
mywin.flip()
# Show the instructions screen
show_instructions(record_duration_int)
# Start the EEG stream
if eeg:
if save_fn is None: # If no save_fn passed, generate a new unnamed save file
# random_id = random.randint(1000,10000)
random_id = 9999
save_fn = generate_save_fn(eeg.device_name, "auditory_erp_arrayin",
random_id, random_id, "unnamed")
print(
f"No path for a save file was passed to the experiment. Saving data to {save_fn}"
)
eeg.start(save_fn, duration=record_duration_float + 5)
# Start EEG Stream, wait for signal to settle, and then pull timestamp for start point
start = time.time()
for ii, trial in trials.iterrows():
# Intertrial interval
time.sleep(trial["iti"])
# Select and play sound
ind = int(trial["sound_ind"])
auds[ind].stop()
auds[ind].play()
additional_stamps = []
for k in additional_labels.keys():
additional_stamps += [trial[k]]
# Send marker
#timestamp = time.time()
# outlet.push_sample([markernames[ind]], timestamp)
#outlet.push_sample(additional_stamps + [markernames[ind]], timestamp)
# Offset
# time.sleep(soa)
# if (time.time() - start) > record_duration:
# break
# Send marker
# outlet.push_sample([markernames[ind]], timestamp)
#outlet.push_sample(additional_stamps + [markernames[ind]], timestamp)
# Push sample
if eeg:
timestamp = time.time()
marker = additional_stamps + [markernames[ind]]
"""
if eeg.backend == "muselsl":
#marker = [markernames[ind]]
marker = additional_stamps + [markernames[ind]]
else:
marker = markernames[ind] # type: ignore
mark
"""
#eeg.push_sample(marker=additional_stamps + marker, timestamp=timestamp)
eeg.push_sample(marker=marker, timestamp=timestamp)
if do_fixation:
mywin.flip()
# offset
core.wait(soa)
mywin.flip()
if len(event.getKeys()) > 0 or (time.time() -
start) > (record_duration_float + 5):
break
event.clearEvents()
# Cleanup
if eeg: eeg.stop()
if do_fixation:
mywin.close()
return trials
def present(duration=120,
eeg: EEG = None,
save_fn=None,
n_trials=2010,
iti=0.4,
soa=0.3,
jitter=0.2):
record_duration = np.float32(duration)
markernames = [1, 2]
# Setup trial list
image_type = np.random.binomial(1, 0.5, n_trials)
trials = DataFrame(
dict(image_type=image_type, timestamp=np.zeros(n_trials)))
def load_image(fn):
return visual.ImageStim(win=mywin, image=fn)
# start the EEG stream, will delay 5 seconds to let signal settle
# Setup graphics
mywin = visual.Window([1600, 900],
monitor="testMonitor",
units="deg",
fullscr=True)
faces = list(
map(load_image, glob(os.path.join(FACE_HOUSE, "faces", "*_3.jpg"))))
houses = list(
map(load_image, glob(os.path.join(FACE_HOUSE, "houses", "*.3.jpg"))))
stim = [houses, faces]
# Show the instructions screen
show_instructions(duration)
if eeg:
if save_fn is None: # If no save_fn passed, generate a new unnamed save file
random_id = random.randint(1000, 10000)
save_fn = generate_save_fn(eeg.device_name, "visual_n170",
random_id, random_id, "unnamed")
print(
f"No path for a save file was passed to the experiment. Saving data to {save_fn}"
)
eeg.start(save_fn, duration=record_duration + 5)
# Start EEG Stream, wait for signal to settle, and then pull timestamp for start point
start = time()
# Iterate through the events
for ii, trial in trials.iterrows():
# Inter trial interval
core.wait(iti + np.random.rand() * jitter)
# Select and display image
label = trials["image_type"].iloc[ii]
image = choice(faces if label == 1 else houses)
image.draw()
# Push sample
if eeg:
timestamp = time()
if eeg.backend == "muselsl":
marker = [markernames[label]]
else:
marker = markernames[label]
eeg.push_sample(marker=marker, timestamp=timestamp)
mywin.flip()
# offset
core.wait(soa)
mywin.flip()
if len(event.getKeys()) > 0 or (time() - start) > record_duration:
break
event.clearEvents()
# Cleanup
if eeg:
eeg.stop()
mywin.close()