def feedback():
for i in range(10):
beforevents, state = bufhelp.buffer_newevents('classifier.prediction',
0,
state=None)
h.set(text='Think of your target letter and get ready')
drawnow()
sleep(2)
h.set(text='', color='k')
drawnow()
sleep(1)
charStim(None)
h.set(text='', color='k')
drawnow()
sleep(1)
feedbackevents, _state = bufhelp.buffer_newevents(
'classifier.prediction', state=state)
# predictions contain both labels and values
chars, predictions = np.array(
[e.value.split('_') for e in feedbackevents]).T
predictions = predictions.astype(float)
# get the character with the highest mean prediction (currently random in feedback stage)
best = max(np.unique(chars),
key=lambda c: np.mean(predictions[chars == c]))
h.set(text=best, color='b')
drawnow()
sleep(2)
def test(text, n_targets=10, repetitions=5):
"""
Online feedback phase
Args:
- text: handle to Text object
- n_targets: Number of iterations to do
- repetitions: Number of times each letter occurs per iteration
"""
letters = list(string.ascii_uppercase)
letter2idx = {l: i for i, l in enumerate(letters)}
# Make sure all classification events are retrieved
bufhelp.MAXEVENTHISTORY = 800
bufhelp.sendEvent('experiment.start', 1)
for target in range(n_targets):
_, state = bufhelp.buffer_newevents('classifier.prediction',
timeout_ms=0,
state=None)
flashes = np.zeros((len(letters), len(letters) * repetitions))
text.set(text='Think of your target letter\nand get ready',
color='green',
fontsize=14)
drawnow()
plt.pause(2)
text.set(text='')
drawnow()
plt.pause(1)
for i, letter in enumerate(np.random.permutation(letters *
repetitions)):
flashes[letter2idx[letter], i] = 1
text.set(text=letter, color='white', fontsize=120)
drawnow()
bufhelp.sendEvent('stimulus.type', 'test')
plt.pause(0.1)
text.set(text='')
# Wait for classifier
plt.pause(3)
predictions, _ = bufhelp.buffer_newevents('classifier.prediction',
state=state,
timeout_ms=5000)
# Make sure the predictions are in the right order
predictions = sorted(predictions, key=lambda p: p.sample)
if len(predictions) != flashes.shape[1]:
print('Too few predictions found..., removing last trials')
flashes = flashes[:, :len(predictions)]
predictions = np.array([p.value[0] for p in predictions])
# Compute inner product of each letter
similarities = flashes @ predictions
# Pick the letter with the highest similarity
letter = np.argmax(similarities)
text.set(text=letters[letter], fontsize=120, color='blue')
plt.pause(2)
bufhelp.sendEvent('experiment.end', 1)
def processBufferEvents():
events = bufhelp.buffer_newevents()
for evt in events:
print(str(evt.sample) + ": " + str(evt))
# keyboard
if evt.type == 'stimulus.prediction':
if evt.value == '@FWRD':
Nao.moveForward(50)
elif evt.value == '@TRNL':
Nao.turnLeft(90)
elif evt.value == '@TRHT':
Nao.turnRight(90)
elif evt.value == '@LEFT':
Nao.moveLeft(90)
elif evt.value == '@RGHT':
Nao.moveRight(90)
elif evt.value == '@BACK':
Nao.moveBackward(50)
elif evt.value == '#HELLO':
Nao.sayHello()
elif evt.value == '#GBYE':
Nao.sayBye()
elif evt.value == '#HWRU':
Nao.sayHRU()
elif evt.value == '#FINE':
Nao.sayFine()
elif evt.value == '*TSK1':
Nao.performTask(0)
elif evt.value == '*TSK2':
Nao.performTask(1)
elif evt.value == '*TSK3':
Nao.performTask(2)
def processBufferEvents():
global running
events = bufhelp.buffer_newevents()
for evt in events:
print(str(evt.sample) + ": " + str(evt))
if evt.type == "classifier.prediction":
pred = evt.value
(m12, i12) = max2(pred) # find max value
if m12[0] - m12[1] > THRESHOLDS[i12[0]]:
send_command(CMDS[i12[0]])
# if above threshold send
elif evt.type == "keyboard":
if evt.value == "q":
send_command(CMD_SPEED)
elif evt.value == "w":
send_command(CMD_JUMP)
elif evt.value == "e":
send_command(CMD_ROLL)
elif evt.value == "esc":
running = false
elif evt.type == "startPhase.cmd":
if evt.value == "quit":
running = False
def action_for_option(self, option):
if option == 8:
print('Going to SOS')
self.controller.sos_trigger()
sos_stimulus = SOSInterfaceStimulus()
sos_stimulus.start_stimulus()
events, self.state = bufhelp.buffer_newevents(
'errp_clsfr.prediction', 1000, self.state)
elif option > 8:
print('Movement')
self.controller.movement(option - 9)
else:
self.controller.phone_tv(option)
def echoClient(timeout=5000):
## init connection to the buffer
ftc,hdr=bufhelp.connect();
# send event to buffer
while True:
events = bufhelp.buffer_newevents(state=True)
for event in events:
if event.type == 'quit':
ftc.disconnect()
return
elif event.type != 'echo':
bufhelp.sendEvent('echo', event.value)
def processBufferEvents():
global running
trlen_ms = 600
events = bufhelp.buffer_newevents()
for evt in events:
if str(evt.type) == 'calibrate' and evt.value == 'start':
print('Calibration phase, gathering data...')
data, events, stopevents = bufhelp.gatherdata("stimulus.target",
750,
("calibrate", "end"),
milliseconds=True)
pickle.dump({
"events": events,
"data": data
}, open("subject_data", "w"))
else:
print(str(evt.sample) + ": " + str(evt))
def processBufferEvents():
global running
events = bufhelp.buffer_newevents()
for evt in events:
print(str(evt.sample) + ": " + str(evt))
if evt.type == 'classifier.prediction':
pred = evt.value
(m12, i12) = max2(pred) # find max value
if m12[0] - m12[1] > THRESHOLDS[i12[0]]:
send_command(CMDS[i12[0]])
# if above threshold send
elif evt.type == 'keyboard':
if evt.value == 'q': send_command(CMD_SPEED)
elif evt.value == 'w': send_command(CMD_JUMP)
elif evt.value == 'e': send_command(CMD_ROLL)
elif evt.value == 'esc': running = false
elif evt.type == 'startPhase.cmd':
if evt.value == 'quit':
running = False
def start_stimulus(self):
# Initialising the state with None doesn't work for some reason. I use this hack
# to get the first value of the state variable
_, state = bufhelp.buffer_newevents('p300.prediction', 1, None)
bufhelp.sendEvent('p300.seq', 'start')
# Start process
# TODO: This should run forever, not just 10 times
done = False
while not done:
self.si.wait(GETREADY_DURATION)
total_sequence = self.single_character_flashing()
# Get prediction event
events, state = bufhelp.buffer_newevents('p300.prediction', 2000,
state)
if events is None or len(events) < 1:
print("Error! no predictions, continuing")
else:
if len(events) > 1:
print("Warning: multiple predictions. Some ignored.")
# only use the last event
evt = events[-1]
# Get letter with best prediction
prediction = self.get_best_by_mean(total_sequence, evt.value)
if DEBUG:
self.all_predictions.append(prediction)
# Display predicted target
bufhelp.sendEvent('stimulus.prediction', prediction)
self.si.flash_option(prediction,
color='blue',
duration=PREDICTION_DURATION)
skip_trial = False
if INCLUDE_ERRP:
# Listen for ErrP classifier signal
events, state = bufhelp.buffer_newevents(
'errp_clsfr.prediction', 1000, state)
# If ErrP detected, display second most confident prediction
if len(events
) >= 1 and events[-1].value >= ERRP_CONFIDENCE:
print('ErrP detected:', prediction)
self.si.flash_option(prediction,
color='red',
duration=ERROR_DURATION)
if RESTART_TRIAL:
print('Skipping this trial')
skip_trial = True
else:
print('Getting next best prediction')
prediction = self.get_best_by_mean(
total_sequence,
evt.value,
exclude=[prediction])
self.si.flash_option(prediction,
color='blue',
duration=PREDICTION_DURATION)
if not skip_trial:
if prediction < 3:
self.controller.sos_panel(prediction)
done = True
self.si.close()
ftc.putEvents(e)
if __name__ == '__main__':
with open('classifier.pkl', 'rb') as f:
clf = pickle.load(f)
ftc, hdr = bufhelp.connect()
trial_length_ms = 600
trial_length_samples = math.ceil((trial_length_ms / 1000.) * hdr.fSample)
# gather stores events that haven't been long enough ago to collect data
gather = []
print('Waiting for events...')
while True:
events = bufhelp.buffer_newevents('stimulus.type', timeout_ms=1000)
for evt in events:
end_sample = evt.sample + trial_length_samples - 1
gather.append((evt, end_sample))
# Current sample count
nSamples = bufhelp.globalstate[0]
for point in gather:
evt, end_sample = point
if end_sample < nSamples:
# Enough time has passed to collect the data
data = ftc.getData((evt.sample, end_sample))
# Preprocess
data = preproc.detrend([data])
data = preproc.spectralfilter(data, (0, 1, 14, 15),
hdr.fSample)
# Load the trained classifier
with open('clsfr.pk', 'rb') as f:
classifier = pickle.load(f)
clf = classifier['classifier']
bad_channels = classifier['bad_channels']
good_channels = np.ones(10, dtype=np.bool)
good_channels[bad_channels] = False
ftc, hdr = bufhelp.connect()
trial_length_samples = math.ceil((trlen_ms / 1000) * hdr.fSample)
# gather stores events while we wait until enough time has passed
gather = []
print('Waiting for events...')
while True:
events = bufhelp.buffer_newevents('experiment.predict',
timeout_ms=1000)
for evt in events:
end_sample = evt.sample + trial_length_samples - 1
gather.append((evt, end_sample))
# Current sample count
nSamples = bufhelp.globalstate[0]
for point in gather:
evt, end_sample = point
if end_sample < nSamples:
# Enough time has passed to collect the data
data = ftc.getData((evt.sample, end_sample))
# Preprocess
data = data[:, :
10] # select channels which were actually connected
data = preproc.detrend([data])
def errp_communicate(menu, position):
selected = False
while True:
events_errp, state_errp = bufhelp.buffer_newevents('errp.prediction',
1000,
state=None)
if events_errp == []:
print("Error! no predictions, continuing")
evt_errp = []
else:
if len(events_errp) > 1:
print("Warning: multiple predictions. Some ignored.")
evt_errp = events_errp[-1] # only use the last event
if evt_errp != []:
if int(evt_errp.value) == 1:
if menu['tv'] and position < 4:
bufhelp.sendEvent('tv', position)
phone_tv_screen(position, blue)
break
if menu['phone'] and position < 4:
bufhelp.sendEvent('call', position)
phone_tv_screen(position, blue)
break
if (menu['phone'] or menu['tv']) and position > 3:
if menu['tv']: bufhelp.sendEvent('tv', 'end')
if position == 4:
menu = menu_dict(4, menu)
selected = True
menu_selecter(menu)
break
if position == 5:
bufhelp.sendEvent('sos', 'on')
menu = menu_dict(4, menu)
selected = True
menu_selecter(menu)
break
if menu['sos']:
if position == 1:
bufhelp.sendEvent('sos', 'pain')
sos_screen(position, blue)
break
if position == 2:
bufhelp.sendEvent('sos', 'food')
sos_screen(position, blue)
break
if position == 3:
bufhelp.sendEvent('sos', 'wc')
sos_screen(position, blue)
break
if position == 4:
selected = True
menu = menu_dict(0, menu)
menu_selecter(menu)
break
if menu['navigation']:
if position == 1:
bufhelp.sendEvent('navigate', 'up')
navigation_screen(position, blue)
break
if position == 2:
bufhelp.sendEvent('navigate', 'down')
navigation_screen(position, blue)
break
if position == 3:
bufhelp.sendEvent('navigate', 'left')
navigation_screen(position, blue)
break
if position == 4:
bufhelp.sendEvent('navigate', 'right')
navigation_screen(position, blue)
break
if position == 5:
selected = True
menu = menu_dict(0, menu)
menu_selecter(menu)
break
if position == 6:
bufhelp.sendEvent('sos', 'on')
selected = True
menu = menu_dict(4, menu)
menu_selecter(menu)
break
if menu['home']:
selected = True
menu = menu_dict(position, menu)
menu_selecter(menu)
break
elif int(evt_errp.value) == 0:
if menu['tv']: phone_tv_screen(position, yellow)
if menu['home']: initial_screen(position, yellow)
if menu['navigation']: navigation_screen(position, yellow)
if menu['sos']: sos_screen(position, yellow)
if menu['phone']: phone_tv_screen(position, yellow)
break
return selected
#wait for key-press to continue
[_.set(color=bgColor) for _ in hdls]
txthdl.set(text='Press key to start')
drawnow()
waitforkey(fig)
txthdl.set(visible=False)
plt.ion()
[ _.set(visible=True) for _ in hdls]
drawnow()
bufhelp.sendEvent('stimulus.feedback','start');
# initialize the state for tracking and catching classifier prediction events
#_,state = bufhelp.buffer_newevents('classifier.prediction',0,False,True)
_ = bufhelp.buffer_newevents('classifier.prediction',0)
## STARTING stimulus loop
for ti in range(nSeq):
sleep(interSeqDuration)
# reset the display
[ _.set(color=bgColor) for _ in hdls]
drawnow()
sleep(postCueDuration)
# start the scanning loop
stimSeq=[] # [ nSymbs x nEpochs ] info on flash/non-flash state of each output
for rep in range(nRep): # repeat enough times
for si in range(nSymbs): # linear scan over outputs
# flash
def start_stimulus(self):
# Initialising the state with None doesn't work for some reason. I use this hack
# to get the first value of the state variable
_, self.state = bufhelp.buffer_newevents('p300.prediction', 1, None)
bufhelp.sendEvent('p300.seq', 'start')
self.si.show_instruction(
'Press any button to start the feedback sequence')
self.si.wait_for_input()
# Start process
while True:
self.si.show_instruction('Think of your target', GETREADY_DURATION)
total_sequence = self.single_character_flashing()
# Get prediction event
events, self.state = bufhelp.buffer_newevents(
'p300.prediction', 1000, self.state)
if events is None or len(events) < 1:
print("Error! no predictions, continuing")
else:
if len(events) > 1:
print("Warning: multiple predictions. Some ignored.")
# only use the last event
evt = events[-1]
# Get letter with best prediction
prediction = self.get_best_by_mean(total_sequence, evt.value)
if DEBUG:
self.all_predictions.append(prediction)
# Display predicted target
bufhelp.sendEvent('stimulus.prediction', str(prediction))
self.si.flash_option(prediction,
color='blue',
duration=PREDICTION_DURATION)
skip_trial = False
if INCLUDE_ERRP:
# Listen for ErrP classifier signal
events, self.state = bufhelp.buffer_newevents(
'errp_clsfr.prediction', 1000, self.state)
# If ErrP detected, display second most confident prediction
if len(events
) >= 1 and events[-1].value <= ERRP_CONFIDENCE:
print('ErrP detected:', prediction)
self.si.flash_option(prediction,
color='red',
duration=ERROR_DURATION)
if RESTART_TRIAL:
print('Skipping this trial')
skip_trial = True
else:
print('Getting next best prediction')
prediction = self.get_best_by_mean(
total_sequence,
evt.value,
exclude=[prediction])
self.si.flash_option(prediction,
color='blue',
duration=PREDICTION_DURATION)
if not skip_trial:
self.action_for_option(prediction)
if DEBUG:
acc = accuracy_score(self.all_predictions, self.all_targets)
print('Accuracy on feedback is: {}'.format(acc))
print(self.all_predictions)
print(self.all_targets)
bufhelp.sendEvent('p300.seq', 'end')
def select_icon_phone_tv_screen(position, menu):
phone_tv_screen(position, yellow)
while True:
if position > 5:
position = 1
phone_tv_screen(position, yellow)
if position < 1:
position = 5
phone_tv_screen(position, yellow)
bufhelp.sendEvent('stimulus.target', "hey")
if not DEBUG: sleep(3)
events, state = bufhelp.buffer_newevents('classifier.prediction',
3000,
state=None)
if events == []:
print("Error! no predictions, continuing")
evt = []
else:
if len(events) > 1:
print("Warning: multiple predictions. Some ignored.")
evt = events[-1] # only use the last event
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
if evt != []:
if int(evt.value) == 0:
position = position - 1
phone_tv_screen(position, yellow)
if int(evt.value) == 1:
position = position + 1
phone_tv_screen(position, yellow)
if int(evt.value) == 2:
if menu["tv"]:
if position == 1:
phone_tv_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 2:
phone_tv_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 3:
phone_tv_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
elif menu['phone']:
if position == 1:
phone_tv_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 2:
phone_tv_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 3:
phone_tv_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 4:
phone_tv_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 5:
phone_tv_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if int(evt.value) == 3:
pass
#wait for key-press to continue
[_.set(color=bgColor) for _ in hdls]
txthdl.set(text='Press key to start')
drawnow()
waitforkey(fig)
txthdl.set(visible=False)
plt.ion()
[_.set(visible=True) for _ in hdls]
drawnow()
bufhelp.sendEvent('stimulus.feedback', 'start')
# initialize the state for tracking and catching classifier prediction events
#_,state = bufhelp.buffer_newevents('classifier.prediction',0,False,True)
_ = bufhelp.buffer_newevents('classifier.prediction', 0)
## STARTING stimulus loop
for ti in range(nSeq):
sleep(interSeqDuration)
# reset the display
[_.set(color=bgColor) for _ in hdls]
drawnow()
sleep(postCueDuration)
# start the scanning loop
stimSeq = [
] # [ nSymbs x nEpochs ] info on flash/non-flash state of each output
for rep in range(nRep): # repeat enough times
for si in range(nSymbs): # linear scan over outputs
def run_feedback(nSequences, nBlock, trialDuration, feedbackDuration, intertrialDuration, baselineDuration, state=None):
# Display fixation circle and two arows (left and right)
initial_display()
# pygame.time.delay(intertrialDuration)
# make the target sequence
nSymbols = 4
targetSequence = list(range(nSymbols)) * int(nSequences / nSymbols + 1) # sequence in sequential order
last_trial_id = len(targetSequence)
shuffle(targetSequence) # N.B. shuffle works in-place!
# 0 - left, 1 - right, 2 - both, 3 - none (no movement)
it = 0
for target in targetSequence:
it +=1
sleep(intertrialDuration)
# show the baseline
pygame.draw.circle(display_surface, blue, [X // 2, Y // 2], 40)
pygame.display.update()
bufhelp.sendEvent('stimulus.baseline', 'start')
sleep(baselineDuration)
bufhelp.sendEvent('stimulus.baseline', 'end')
# initial_display()
# show the target in yellow
if target == 0:
pygame.draw.circle(display_surface, yellow, [X // 2, Y // 2], 40) # fixation yellow
pygame.draw.circle(display_surface, yellow, (535, 400), 100) # mark target
display_surface.blit(left_arr, (471, 336))
pygame.display.update()
elif target == 1:
pygame.draw.circle(display_surface, yellow, [X // 2, Y // 2], 40) # fixation yellow
pygame.draw.circle(display_surface, yellow, (1065, 400), 100) # mark target
display_surface.blit(right_arr, (1001, 336))
pygame.display.update()
elif target == 2:
pygame.draw.circle(display_surface, yellow, [X // 2, Y // 2], 40) # fixation yellow
pygame.draw.circle(display_surface, yellow, (535, 400), 100) # mark target
pygame.draw.circle(display_surface, yellow, (1065, 400), 100) # mark target
display_surface.blit(left_arr, (471, 336))
display_surface.blit(right_arr, (1001, 336))
pygame.display.update()
else:
pygame.draw.circle(display_surface, yellow, [X // 2, Y // 2], 40) # fixation yellow
pygame.display.update()
bufhelp.sendEvent('stimulus.trial', 'start')
if it == last_trial_id :
bufhelp.sendEvent('stimulus.last_target', target)
else:
bufhelp.sendEvent('stimulus.target', target)
injectERP(amp=1)
sleep(trialDuration)
bufhelp.sendEvent('stimulus.end_target', 'end')
#catch the prediction
# N.B. use state to track which events processed so far
events,state = bufhelp.buffer_newevents('classifier.prediction',3000,state)
if events == []:
print("Error! no predictions, continuing")
else:
if len(events)>1:
print("Warning: multiple predictions. Some ignored.")
evt=events[-1] # only use the last event
print(evt.value == target)
if evt.value == target:
bufhelp.sendEvent('stimulus.start_feedback', "1")
else:
bufhelp.sendEvent('stimulus.start_feedback', "0")
# show prediction (user feedback)
if evt.value == target : # good prediction !
# Turn the target green
if target == 0:
pygame.draw.circle(display_surface, green, [X // 2, Y // 2], 40)
pygame.draw.circle(display_surface, green, (535, 400), 100)
display_surface.blit(left_arr, (471, 336))
pygame.display.update()
elif target == 1:
pygame.draw.circle(display_surface, green, [X // 2, Y // 2], 40)
pygame.draw.circle(display_surface, green, (1065, 400), 100)
display_surface.blit(right_arr, (1001, 336))
pygame.display.update()
elif target == 2:
pygame.draw.circle(display_surface, green, [X // 2, Y // 2], 40)
pygame.draw.circle(display_surface, green, (535, 400), 100)
pygame.draw.circle(display_surface, green, (1065, 400), 100)
display_surface.blit(left_arr, (471, 336))
display_surface.blit(right_arr, (1001, 336))
pygame.display.update()
else:
pygame.draw.circle(display_surface, green, [X // 2, Y // 2], 40)
pygame.display.update()
else : # wrong prediction !
# Turn target red
if target == 0:
pygame.draw.circle(display_surface, red, [X // 2, Y // 2], 40)
pygame.draw.circle(display_surface, red, (535, 400), 100)
display_surface.blit(left_arr, (471, 336))
pygame.display.update()
elif target == 1:
pygame.draw.circle(display_surface, red, [X // 2, Y // 2], 40)
pygame.draw.circle(display_surface, red, (1065, 400), 100)
display_surface.blit(right_arr, (1001, 336))
pygame.display.update()
elif target == 2:
pygame.draw.circle(display_surface, red, [X // 2, Y // 2], 40)
pygame.draw.circle(display_surface, red, (535, 400), 100)
pygame.draw.circle(display_surface, red, (1065, 400), 100)
display_surface.blit(left_arr, (471, 336))
display_surface.blit(right_arr, (1001, 336))
pygame.display.update()
else:
pygame.draw.circle(display_surface, red, [X // 2, Y // 2], 40)
pygame.display.update()
injectERP(amp=1)
sleep(feedbackDuration)
bufhelp.sendEvent('stimulus.end_feedback', "end")
bufhelp.sendEvent('stimulus.trial', 'end')
# reset the display
initial_display()
sleep(1)
def select_icon_navigation_screen(position, menu):
navigation_screen(position, yellow)
while True:
if position > 6:
position = 1
navigation_screen(position, yellow)
if position < 1:
position = 6
navigation_screen(position, yellow)
bufhelp.sendEvent('stimulus.target', "hey")
if not DEBUG: sleep(3)
events, state = bufhelp.buffer_newevents('classifier.prediction',
3000,
state=None)
if events == []:
print("Error! no predictions, continuing")
evt = []
else:
if len(events) > 1:
print("Warning: multiple predictions. Some ignored.")
evt = events[-1] # only use the last event
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
if evt != []:
if int(evt.value) == 0:
position = position - 1
navigation_screen(position, yellow)
# if right movement is predicted
if int(evt.value) == 1:
position = position + 1
navigation_screen(position, yellow)
# if both hands movement is predicted - select this option
if int(evt.value) == 2:
if position == 1:
navigation_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 2:
navigation_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 3:
navigation_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if position == 4:
navigation_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
# home selected
if position == 5:
navigation_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
# sos selected
if position == 6:
navigation_screen(position, green)
bufhelp.sendEvent('errp.trigger', 'start')
if not DEBUG: sleep(1)
selected = errp_communicate(menu, position)
if selected: break
if int(evt.value) == 3:
pass
## init connection to the buffer
ftc,hdr=bufhelp.connect();
#wait for key-press to continue
[_.set(facecolor=bgColor) for _ in hdls]
txthdl.set(text='Press key to start')
drawnow()
waitforkey(fig)
bufhelp.sendEvent('stimulus.training','start')
state=None
## STARTING stimulus loop
for si,tgt in enumerate(tgtSeq):
# YOUR CODE HERE #
#catch the prediction
# N.B. use state to track which events processed so far
events,state = bufhelp.buffer_newevents('classifier.prediction',1500,state)
# reset the display
hdls[-1].set_xy(stimPos[-1,:])
[ _.set(visible=False) for _ in hdls]
txthdl.set(visible=False)
drawnow()
bufhelp.sendEvent('stimulus.trial','end');
bufhelp.sendEvent('stimulus.training','end')
txthdl.set(text=['Thanks for taking part!' '' 'Press key to finish'])
waitforkey(fig)
keys[event.key] = down
if event.type == QUIT or keys[K_ESCAPE]:
sys.exit()
screen.fill(BACKGROUND_COLOR)
# check if an enemy needs to spawn
if curtime - last_enemy_spawned > ENEMY_SPAWN_TIME:
enemy_group.add(EnemySprite(left))
left = not left
last_enemy_spawned = curtime
if not SIMULATION and curtime - last_pred_time > PREDICTION_TIME and isinstance(controller, PlayerController):
# Ask for a new prediction and process the predictions received from the BCI
last_pred_time = curtime
bufhelp.sendEvent('experiment.predict', 1)
events = bufhelp.buffer_newevents('classifier.prediction', timeout_ms=0.01)
for event in events:
print(f'Got prediction event: {event.value}')
controller.move(event.value[0])
ship_start_pos = ship.position[0]
if SIMULATION and curtime - last_pred_time > PREDICTION_TIME and isinstance(controller, ProbablisticConroller):
# Use the probabilistic controller
last_pred_time = curtime
if len(enemy_group) > 0:
lowest_left = max(enemy_group, key=lambda e: e.rect.bottom if hasattr(e, 'rect') else 0).left
controller.target_position = -1 if lowest_left else 1
controller.move(0)
ship_start_pos = ship.position[0]
