def echoClient(timeout=5000):
## init connection to the buffer
ftc,hdr=bufhelp.connect();
# Now do the echo client
nEvents=hdr.nEvents;
endExpt=None;
while endExpt is None:
bufhelp.sendEvent('echo',1)
# wait for ackknowledgement from Server
ack_received = False
while ack_received is False:
(curSamp,curEvents)=ftc.wait(-1,nEvents,timeout) # Block until there are new events to process
if curEvents>nEvents :
evts=ftc.getEvents([nEvents,curEvents-1])
nEvents=curEvents # update record of which events we've seen
for evt in evts:
if evt.type == "exit": endExpt=1
if evt.type == "ack": ack_received = True
print(evt)
else:
print("Waiting for acknowledgement...")
ftc.disconnect() # disconnect from buffer when done
def echoClient(timeout=5000):
## init connection to the buffer
ftc, hdr = bufhelp.connect()
# Now do the echo client
nEvents = hdr.nEvents
endExpt = None
while endExpt is None:
bufhelp.sendEvent('echo', 1)
# wait for ackknowledgement from Server
ack_received = False
while ack_received is False:
(curSamp, curEvents) = ftc.wait(
-1, nEvents,
timeout) # Block until there are new events to process
if curEvents > nEvents:
evts = ftc.getEvents([nEvents, curEvents - 1])
nEvents = curEvents # update record of which events we've seen
for evt in evts:
if evt.type == "exit": endExpt = 1
if evt.type == "ack": ack_received = True
print(evt)
else:
print("Waiting for acknowledgement...")
ftc.disconnect() # disconnect from buffer when done
def single_character_flashing(self):
"Runs the single charachter flashing sequence"
options = list(range(self.num_of_options))
if DEBUG:
fake_target = choice(options)
print('Fake target', fake_target)
total_sequence = np.array([], dtype=np.int16)
for i in range(NUM_ROUNDS):
shuffle(options)
total_sequence = np.append(total_sequence, options)
for o in options:
bufhelp.sendEvent('p300.flash', o)
if DEBUG:
if o == fake_target:
self.inject_erp()
self.si.flash_option(o, color='red', duration=FLASH_DURATION)
# Wait some more for the last characters to be recorded
self.si.wait(CLEAR_DURATION)
if DEBUG:
print('Fake target', fake_target)
self.all_targets.append(fake_target)
bufhelp.sendEvent('p300.singleseq', 'end')
return total_sequence
def run_calibration(nSequences, nBlock, trialDuration, intertrialDuration, baselineDuration):
# 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
shuffle(targetSequence) # N.B. shuffle works in-place!
# 0 - left, 1 - right, 2 - both, 3 - none (no movement)
for target in targetSequence:
sleep(intertrialDuration)
# show the baseline
pygame.draw.circle(display_surface, red, [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
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')
bufhelp.sendEvent('stimulus.target', target)
injectERP(amp=1)
sleep(trialDuration)
# reset the display
initial_display()
bufhelp.sendEvent('stimulus.trial', 'end');
def send_prior_loop(events, timeout=2):
## init connection to the buffer
ftc, hdr = bufhelp.connect()
ev_list = []
# Now do the echo client
nEvents = hdr.nEvents
endExpt = None
bufhelp.sendEvent('prior.start', '1')
priors = calc_priors(ev_list)
bufhelp.sendEvent('priors', priors.to_csv())
priors.to_csv('priors.csv')
# bufhelp.sendEvent('p300preds', priors.to_csv())
i = 0
while endExpt is None:
(curSamp, curEvents) = ftc.wait(
-1, nEvents,
timeout) # Block until there are new events to process
if curEvents > nEvents:
evts = ftc.getEvents([nEvents, curEvents - 1])
nEvents = curEvents # update record of which events we've seen
for evt in evts:
if evt.type == "exit": endExpt = 1
if evt.type == "p300preds":
# read classifier predictions
preds = pd.read_csv(StringIO(evt.value),
header=None).set_index(0)[1]
preds = preds.drop('pause')
preds.to_csv('p300pred_wouter' + str(i) + '.csv')
i += 1
print(priors)
print('preds', preds)
p = preds * priors
print(p)
p /= sum(p)
print(p)
best = p.sort_values().index[-1]
best_event, best_value = best.split('.')
if best_event == 'sos':
bufhelp.sendEvent('sos', 'start')
# send actual event to virtual environment
bufhelp.sendEvent(best_event, best_value)
# send event to MatLab code
bufhelp.sendEvent('finalprediction',
best_event + '.' + best_value)
ev_list.append(best)
priors = calc_priors(ev_list)
priors.to_csv('priors.csv')
# print(evt)
ftc.disconnect() # disconnect from buffer when done
def echoClient(timeout=5000):
## init connection to the buffer
ftc, hdr = bufhelp.connect()
# send event to buffer
bufhelp.sendEvent('echo', 1)
# wait for ackknowledgement from Server
ftc.disconnect() # disconnect from buffer when done
def echoClient(timeout=5000):
## init connection to the buffer
ftc,hdr=bufhelp.connect();
# send event to buffer
bufhelp.sendEvent('echo',1)
# wait for ackknowledgement from Server
ftc.disconnect() # disconnect from buffer when done
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 charStim(t):
for i in range(5):
h.set(text='')
drawnow()
np.random.shuffle(letters)
sleep(1)
print(letters)
bufhelp.sendEvent('stimulus.repetition', i)
for c in letters:
bufhelp.sendEvent('stimulus.char',
c if t == None else c + '_' + str(c == t))
if c == t:
injectERP(1)
h.set(text=c)
drawnow() # update the display
sleep(.1)
def calibrate(text, n_targets=10, repetitions=5):
"""
Calibration phase
Args:
- window: Render the stimuli to this window
- n_targets: Number of iterations with a random target letter to do
- repetitions: Number of times each letter occurs per iteration
"""
letters = list(string.ascii_uppercase)
target_letters = np.random.choice(letters, size=n_targets)
bufhelp.sendEvent('experiment.start', 1)
for target in target_letters:
text.set(text=target, color='green')
drawnow()
plt.pause(2)
text.set(text='')
drawnow()
plt.pause(1)
for letter in np.random.permutation(letters * repetitions):
text.set(text=letter, color='white')
drawnow()
stimulus_name = int(letter == target)
bufhelp.sendEvent('stimulus.type', stimulus_name)
plt.pause(0.1)
bufhelp.sendEvent('experiment.end', 1)
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 calibration():
targets = np.random.choice(letters, 10, replace=False)
bufhelp.sendEvent('stimulus.targets', targets)
for t in targets:
bufhelp.sendEvent('stimulus.target', t)
h.set(text=t, color='g')
drawnow()
sleep(2)
h.set(text='', color='k')
drawnow()
sleep(1)
charStim(t)
h.set(text='', color='k')
drawnow()
sleep(1)
bufhelp.sendEvent('stimulus.training', 'end')
def single_character_flashing(target, bufhelp, num_of_options):
"Runs the single charachter flashing sequence"
options = list(range(num_of_options))
total_sequence = np.array([], dtype=np.int16)
for i in range(NUM_ROUNDS):
shuffle(options)
total_sequence = np.append(total_sequence, options)
for o in options:
bufhelp.sendEvent('stimulus.letter', o)
bufhelp.sendEvent('stimulus.cue',
'target' if o == target else 'nontarget')
if (o == target and DEBUG):
inject_erp()
si.flash_option(o, color='red', duration=FLASH_DURATION)
si.wait(INTERSTIMULUS_DURATION)
# Wait some more for the last characters to be recorded
#si.wait(4)
bufhelp.sendEvent('stimulus.singleseq', 'end')
return total_sequence
interCharDuration = 1
ftc, hdr = bufhelp.connect()
# set the display and the string for stimulus
fig = plt.figure()
fig.suptitle('RunSentences-Stimulus', fontsize=24, fontweight='bold')
ax = fig.add_subplot(111) # default full-screen ax
ax.set_xlim((-1, 1))
ax.set_ylim((-1, 1))
ax.set_axis_off()
h = ax.text(0, 0, '', style='italic')
## init connection to the buffer
bufhelp.sendEvent('stimulus.letters', 'start')
## STARTING PROGRAM LOOP
drawnow() # update the display
# inject a signal into the simulated data-stream for debugging your signal-processing scripts
# sleep(interCharDuration)
##------------------------------------------------------
## ADD YOUR CODE BELOW HERE
##------------------------------------------------------
def charStim(t):
for i in range(5):
h.set(text='')
circ = patches.Circle((0, 0), stimRadius / 4, facecolor=bgColor)
hhi = ax.add_patch(circ)
hdls.insert(nSymbs, hhi)
[_.set(visible=False) for _ in hdls] # make all invisible
## 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)
txthdl.set(visible=False)
bufhelp.sendEvent('stimulus.training', 'start')
## STARTING stimulus loop
for si, tgt in enumerate(tgtSeq):
sleep(intertrialDuration)
# show the baseline
hdls[-1].set(visible=True, facecolor=fixColor)
drawnow()
bufhelp.sendEvent('stimulus.baseline', 'start')
sleep(baselineDuration)
bufhelp.sendEvent('stimulus.baseline', 'end')
#show the target
print("%d) tgt=%d :" % (si, tgt))
[_.set(facecolor=bgColor, visible=True) for _ in hdls]
mywin = visual.Window(size=(screenWidth, screenHeight), fullscr=False, screen=1, allowGUI=False, allowStencil=False,
monitor='testMonitor', units="pix",color=[1,1,1], colorSpace='rgb',blendMode='avg', useFBO=True)
#define variables
sentence = ['Hello World!', 'How are you today?', 'Good, thank you!']
interCharDuration=1
interSentenceDuration=5
#create some stimuli
welcome_text = visual.TextStim(mywin, text='Welcome! \n\nPress a key to start...',color=(-1,-1,-1),wrapWidth = 800)
current_text = visual.TextStim(mywin, text='',color=(-1,-1,-1),wrapWidth = 800)
goodbye_text = visual.TextStim(mywin, text='Bye! \n\nPress a key to finish...',color=(-1,-1,-1),wrapWidth = 800)
# ************** Start run sentences **************
showText(welcome_text)
bufhelp.sendEvent('experiment','start')
waitForKeypress()
bufhelp.sendEvent('stimulus.seq','start')
for s in range(len(sentence)):
current_sentence = sentence[s]
bufhelp.sendEvent('stimulus.sentence','start')
for i in range(len(current_sentence)):
for c in range(i+1):
current_text.text = current_text.text + current_sentence[c]
showText(current_text)
bufhelp.sendEvent('stimulus.letter',current_sentence[i])
current_text.text = '';
core.wait(interCharDuration)
bufhelp.sendEvent('stimulus.sentence','end')
mywin.flip()
rowh=[]
# TODO: ensure row is enumeratable...
for j,symb in enumerate(row):
y=axh[0]+j*h+h/2
print('%s @(%f,%f)'%(symb,x,y))
txthdl =ax.text(x, y, symb, color=txtColor,visible=True)
rowh.append(txthdl)
if len(rowh)==1: rowh=rowh[0] # BODGE: ensure hdls has same structure as symbols
hdls.append(rowh)
print('hds(%d)=[%s]'%(len(hdls),str(hdls)))
drawnow()
return hdls
##--------------------- Start of the actual experiment loop ----------------------------------
bufhelp.sendEvent('stimulus.feedback','start');
state = None
## STARTING stimulus loop
#catch the prediction
# N.B. use state to track which events processed so far
events,state = bufhelp.buffer_newevents('classifier.prediction',5000,state)
if events == []:
print("Error! no predictions, continuing")
else:
# get all true stimulus into 1 numpy array
stimSeq = np.array(stimSeq) # [ nEpochs x nSymbs ]
# get all predictions into 1 numpy array
pred = np.array([e.value for e in events]) # [ nEpochs ]
ss = stimSeq[:len(pred),:] # [ nSymbs x nEpochs ]
interCharDuration=1;
##--------------------- Start of the actual experiment loop ----------------------------------
# set the display and the string for stimulus
fig = plt.figure()
fig.suptitle('RunSentences-Stimulus', fontsize=14, fontweight='bold')
ax = fig.add_subplot(111) # default full-screen ax
ax.set_xlim((-1,1))
ax.set_ylim((-1,1))
ax.set_axis_off()
txthdl = ax.text(0, 0, 'This is some text', style='italic')
## init connection to the buffer
ftc,hdr=bufhelp.connect();
bufhelp.sendEvent('stimulus.sentences','start');
## STARTING PROGRAM LOOP
for si,sentence in enumerate(sentences):
# reset the display
txthdl.set(text='')
drawnow()
bufhelp.sendEvent('stimulus.sentence',sentence)
for ci in range(len(sentence)):
char = sentence[ci]
print("%d) char=%s sentence=%s"%(ci,char,sentence[0:ci+1]))
txthdl.set(text=sentence[0:ci+1])
drawnow()
bufhelp.sendEvent('stimulus.character',char)
injectERP(1) # inject erp into data = should see in sig-viewer / event-locked average
for ti in range(0,nSymbs):
t_theta=2*math.pi*ti/(nSymbs+1)
t_center[ti]=(int(math.cos(t_theta)*winRect.width*.3)+winRect.centerx,
-int(math.sin(t_theta)*winRect.height*.3)+winRect.centery)
t_center[nSymbs]=winRect.center # fixation point
##--------------------- Start of the actual experiment loop ----------------------------------
drawString(["Motor Imagery Experiment" "Feedback phase" "" "Perform the Green cued task" "after trial feedback given in blue" "" "Key to continue"])
pygame.display.update() # drawnow equivalent...
waitForKey()
# make the target sequence
tgtSeq = range(0,nSymbs)*nSeq
random.shuffle(tgtSeq)
bufhelp.sendEvent('stimulus.testing','start')
events,state = buffer_newevents(timeout_ms=0) # initialize event queue
## STARTING STIMULUS LOOP
for si in range(0,nSeq):
# reset the display
drawString('')
pygame.display.update() # drawnow equivalent...
time.sleep(intertrialDuration)
# reset with red fixation to alert to trial start
drawTrial([t_center[-1]])
pygame.display.update()
bufhelp.sendEvent('stimulus.baseline','start')
time.sleep(baselineDuration)
bufhelp.sendEvent('stimulus.baseline','end')
for ti in range(0,nSymbs):
t_theta=2*math.pi*ti/(nSymbs+1)
t_center[ti]=(int(math.cos(t_theta)*winRect.width*.3)+winRect.centerx,
-int(math.sin(t_theta)*winRect.height*.3)+winRect.centery)
t_center[nSymbs]=winRect.center # fixation point
##--------------------- Start of the actual experiment loop ----------------------------------
drawString(["Motor Imagery Experiment" "Feedback phase" "" "Perform the Green cued task" "after trial feedback given in blue" "" "Key to continue"])
pygame.display.update() # drawnow equivalent...
waitForKey()
# make the target sequence
tgtSeq = range(0,nSymbs)*nSeq
random.shuffle(tgtSeq)
bufhelp.sendEvent('stimulus.testing','start')
events,state = buffer_newevents(timeout_ms=0) # initialize event queue
## STARTING STIMULUS LOOP
for si in range(0,nSeq):
# reset the display
drawString('')
pygame.display.update() # drawnow equivalent...
time.sleep(intertrialDuration)
# reset with red fixation to alert to trial start
drawTrial([t_center[-1]])
pygame.display.update()
bufhelp.sendEvent('stimulus.baseline','start')
time.sleep(baselineDuration)
bufhelp.sendEvent('stimulus.baseline','end')
interSentenceDuration = 3
interCharDuration = 1
##------------------------------------------------------
## ADD YOUR CODE BELOW HERE
##------------------------------------------------------
# set the display and the string for stimulus
fig = plt.figure()
fig.suptitle('RunSentences-Stimulus', fontsize=24, fontweight='bold')
ax = fig.add_subplot(111) # default full-screen ax
ax.set_xlim((-1, 1))
ax.set_ylim((-1, 1))
ax.set_axis_off()
h = ax.text(0, 0, 'This is some text', style='italic')
## init connection to the buffer
ftc, hdr = bufhelp.connect()
bufhelp.sendEvent('stimulus.sentences', 'start')
## STARTING PROGRAM LOOP
h.set_text('some_text')
drawnow() # update the display
# inject a signal into the simulated data-stream for debugging your signal-processing scripts
injectERP(1)
sleep(interCharDuration)
waitforkey(fig)
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])
data[0] = data[0][:, good_channels] # filter out bad-channels
data = preproc.spatialfilter(data, type='car')
data = preproc.spectralfilter(data, (5, 6, 31, 32),
hdr.fSample)
freqs = np.linspace(0, hdr.fSample / 2, len(data[0]))
data = [d[(5 <= freqs) & (freqs <= 32)] for d in data]
data[0] = util.subsample_frequencies(data[0],
width=13).reshape(-1)
# This event is processed, so it can be removed
gather.remove(point)
# Classify
data[0] = data[0]
pred = clf.predict_proba(data)[0][1]
print(pred)
# Send back event
bufhelp.sendEvent('classifier.prediction', pred)
## init connection to the buffer
ftc,hdr=bufhelp.connect();
#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
score = n_shots = n_deaths = n_hits = 0
rect = screen.get_rect()
ship = ShipSprite()
ship_group = pygame.sprite.RenderPlain(ship)
controller = ProbablisticConroller(alpha=0.5*PREDICTION_TIME) if SIMULATION else PlayerController()
enemy_group = pygame.sprite.RenderPlain()
bullet_group = pygame.sprite.RenderPlain()
last_enemy_spawned = -ENEMY_SPAWN_TIME
ship_start_pos = 0
last_pred_time = 0
left = True
pygame.init()
font = pygame.font.Font(pygame.font.get_default_font(), 16)
lasttime = last_bullet_spawned = game_start_time = time.time()
if not SIMULATION: bufhelp.sendEvent('experiment.im', 'start')
while True:
clock.tick(60) # 60 FPS
curtime = time.time()
deltatime = curtime - lasttime
lasttime = curtime
# Record which keys are pressed and check if the game should exit
for event in pygame.event.get():
if not hasattr(event, 'key'): continue
down = event.type == KEYDOWN
keys[event.key] = down
if event.type == QUIT or keys[K_ESCAPE]:
sys.exit()
screen.fill(BACKGROUND_COLOR)
# setup the targets
hdls=initGrid(symbols)
[ _.set(visible=False) for _ in hdls] # make all invisible
## 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)
txthdl.set(visible=False)
bufhelp.sendEvent('stimulus.training','start');
## STARTING stimulus loop
for si,tgt in enumerate(tgtSeq):
sleep(interSeqDuration)
# start the scanning loop
for rep in range(nRep):
for si in range(nSymbs):
# flash
hdls[si].set(color=flashColor)
drawnow()
bufhelp.sendEvent('stimulus.cue',si)
bufhelp.sendEvent('stimulus.tgtFlash',si==tgt)
sleep(stimDuration)
# reset
hdls=initGrid(symbols)
[ _.set(visible=False) for _ in hdls] # make all invisible
## init connection to the buffer
ftc,hdr=bufhelp.connect();
#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()
bufhelp.sendEvent('stimulus.training','start');
## START EXPERIMENTAL LOOP #
#show the target/cue
print("%d) tgt=%d :"%(ti,tgt))
[_.set(color=bgColor,visible=True) for _ in hdls]
hdls[tgt].set(color=tgtColor)
drawnow()
bufhelp.sendEvent('stimulus.target',tgt)
sleep(cueDuration)
# reset the display
[ _.set(color=bgColor) for _ in hdls]
drawnow()
# get data in correct format
data = np.transpose(data)
# 1: detrend
data = preproc.detrend(data)
# 2: bad-channel removal (as identifed in classifier training)
data = data[goodch, :, :]
# 3: apply spatial filter (as in classifier training)
data = preproc.spatialfilter(data, type=spatialfilter)
# 4: map to frequencies (TODO: check fs matches!!)
data, freqs = preproc.powerspectrum(data, dim=1, fSample=fs)
# 5: select frequency bins we want
data, freqIdx = preproc.selectbands(data,
dim=1,
band=freqbands,
bins=freqs)
print(data.shape)
# 6: apply the classifier, get raw predictions
X2d = np.reshape(
data,
(-1,
data.shape[2])).T # sklearn needs data to be [nTrials x nFeatures]
fraw = classifier.predict(X2d)
# 7: map from fraw to event values
# predictions = [ ivaluedict[round(i)] for i in fraw ]
predictions = fraw
# send the prediction events
for letter, pred in zip(event_letters, predictions):
bufhelp.sendEvent("classifier.prediction", letter + '_' + str(pred))
print(pred)
stimRadius=.5;
theta=np.linspace(0,np.pi,nSymbs)
stimPos=np.stack((np.cos(theta),np.sin(theta))) #[2 x nSymbs]
for hi,pos in enumerate(stimPos):
rect=patches.Rectangle((pos[0]-stimRadius/2,pos[1]-stimRadius/2),stimRadius/2,stimRadius/2,facecolor=bgColor)
hhi=ax.add_patch(rect)
hdls.insert(hi,hhi)
# add symbol for the center of the screen
spos = np.array((0,0)).reshape((-1,1))
stimPos=np.hstack((stimPos,spos)) #[2 x nSymbs+1]
rect = patches.Rectangle((0-stimRadius/4,0-stimRadius/4),stimRadius/2,stimRadius/2,facecolor=bgColor)
hhi =ax.add_patch(rect)
hdls.insert(nSymbs,hhi)
[ _.set(visible=False) for _ in hdls] # make all invisible
## init connection to the buffer
ftc,hdr=bufhelp.connect();
#---------------------------------------------------------------------------------------
# YOUR CODE BELOW HERE
#---------------------------------------------------------------------------------------
#wait for key-press to continue
[_.set(facecolor=bgColor) for _ in hdls] # set color for all boxes
txthdl.set(text='Press key to start',visible=True) # set the text info and make visible
drawnow()
waitforkey(fig) # wait for a keypress
bufhelp.sendEvent('stimulus.training','start') # send an event
while len(allKeys)==0:
allKeys = event.getKeys()
if 'escape' in allKeys[0]:
mywin.close() # quit
core.quit()
# Setup the stimulus window
screenWidth = 1300
screenHeight = 700
mywin = visual.Window(size=(screenWidth, screenHeight), fullscr=False, screen=1, allowGUI=False, allowStencil=False,
monitor='testMonitor', units="pix",color=[1,1,1], colorSpace='rgb',blendMode='avg', useFBO=True)
#create some stimuli
current_text = visual.TextStim(mywin, text='blah',color=(-1,-1,-1),wrapWidth = 800)
# ************** Start run sentences **************
# show some text
showText(current_text)
# label data
bufhelp.sendEvent('experiment','start')
# pause (in secs)
core.wait(1)
for j, symb in enumerate(row):
y = axh[0] + j * h + h / 2
print('%s @(%f,%f)' % (symb, x, y))
txthdl = ax.text(x, y, symb, color=txtColor, visible=True)
rowh.append(txthdl)
if len(rowh) == 1:
rowh = rowh[0] # BODGE: ensure hdls has same structure as symbols
hdls.append(rowh)
print('hds(%d)=[%s]' % (len(hdls), str(hdls)))
drawnow()
return hdls
##--------------------- Start of the actual experiment loop ----------------------------------
bufhelp.sendEvent('stimulus.feedback', 'start')
state = None
## STARTING stimulus loop
#catch the prediction
# N.B. use state to track which events processed so far
events, state = bufhelp.buffer_newevents('classifier.prediction', 5000, state)
if events == []:
print("Error! no predictions, continuing")
else:
# get all true stimulus into 1 numpy array
stimSeq = np.array(stimSeq) # [ nEpochs x nSymbs ]
# get all predictions into 1 numpy array
pred = np.array([e.value for e in events]) # [ nEpochs ]
ss = stimSeq[:len(pred), :] # [ nSymbs x nEpochs ]
interCharDuration = 1
##--------------------- Start of the actual experiment loop ----------------------------------
# set the display and the string for stimulus
fig = plt.figure()
fig.suptitle('RunSentences-Stimulus', fontsize=14, fontweight='bold')
ax = fig.add_subplot(111) # default full-screen ax
ax.set_xlim((-1, 1))
ax.set_ylim((-1, 1))
ax.set_axis_off()
txthdl = ax.text(0, 0, 'This is some text', style='italic')
## init connection to the buffer
ftc, hdr = bufhelp.connect()
bufhelp.sendEvent('stimulus.sentences', 'start')
## STARTING PROGRAM LOOP
for si, sentence in enumerate(sentences):
# reset the display
txthdl.set(text='')
drawnow()
bufhelp.sendEvent('stimulus.sentence', sentence)
for ci in range(len(sentence)):
char = sentence[ci]
print("%d) char=%s sentence=%s" % (ci, char, sentence[0:ci + 1]))
txthdl.set(text=sentence[0:ci + 1])
drawnow()
bufhelp.sendEvent('stimulus.character', char)
sleep(interCharDuration)
hdls = initGrid(symbols)
[_.set(visible=False) for _ in hdls] # make all invisible
## init connection to the buffer
ftc, hdr = bufhelp.connect()
#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()
bufhelp.sendEvent('stimulus.training', 'start')
## STARTING stimulus loop
for si, tgt in enumerate(tgtSeq):
sleep(interSeqDuration)
#show the target/cue
print("%d) tgt=%d :" % (si, tgt))
[_.set(color=bgColor, visible=True) for _ in hdls]
hdls[tgt].set(color=tgtColor)
drawnow()
bufhelp.sendEvent('stimulus.target', tgt)
bufhelp.sendEvent('stimulus.trial', 'start')
sleep(cueDuration)
# reset the display
screenWidth = 1300
screenHeight = 700
mywin = visual.Window(size=(screenWidth, screenHeight),
fullscr=False,
screen=1,
allowGUI=False,
allowStencil=False,
monitor='testMonitor',
units="pix",
color=[1, 1, 1],
colorSpace='rgb',
blendMode='avg',
useFBO=True)
#create some stimuli
current_text = visual.TextStim(mywin,
text='blah',
color=(-1, -1, -1),
wrapWidth=800)
# ************** Start run sentences **************
# show some text
showText(current_text)
# label data
bufhelp.sendEvent('experiment', 'start')
# pause (in secs)
core.wait(1)
# combine predictions from classifiers
if prediction == [0, 0]:
prediction_int = 3
if prediction == [1, 0]:
prediction_int = 0
if prediction == [0, 1]:
prediction_int = 1
if prediction == [1, 1]:
prediction_int = 2
print(prediction , prediction_int, pred)
threshold.append([events_im[0].value, prediction0 , prediction_int, pred, data_rec])
bufhelp.sendEvent('classifier.prediction', str(prediction_int))
# stop processing if needed
if "stimulus.last_target" in event_types :
break
else:
# wait for data after a trigger event
# exitevent=None means return as soon as data is ready
# N.B. be sure to propogate state between calls
data_im, events_im, stopevents, pending = bufhelp.gatherdata(["stimulus.target", "stimulus.last_target"],im_length,[], milliseconds=True)
print('asd')
# get all event type labels
event_types = [e.type for e in events_im]
print(event_types)
# get all event values
# get all event type labels
event_types = [e.type[0] for e in events]
# stop processing if needed
if "stimulus.feedback" in event_types:
break
print("Applying classifier to %d events"%(len(events)))
# get data in correct format
data = np.transpose(data) # make it [d x tau]
# 1: detrend
data = preproc.detrend(data)
# 2: bad-channel removal (as identifed in classifier training)
data = data[goodch,:,:]
# 3: apply spatial filter (as in classifier training)
data = preproc.spatialfilter(data,type=spatialfilter)
# 4 & 5: spectral filter (TODO: check fs matches!!)
data = preproc.fftfilter(data, 1, freqbands, fs)
# 6 : bad-trial removal
# 7: apply the classifier, get raw predictions
X2d = np.reshape(data,(-1,data.shape[2])).T # sklearn needs data to be [nTrials x nFeatures]
fraw = classifier.predict(X2d)
# 8: map from fraw to event values (note probably not necessary here!)
#predictions = [ ivaluedict[round(i)] for i in fraw ]
# send the prediction events
for i,f in enumerate(fraw):
print("%d) {%s}=%f(raw)\n"%(i,str(events[i]),f))
bufhelp.sendEvent("classifier.prediction",f)
# stop processing if needed
if "stimulus.feedback" in event_types:
break
print("Applying classifier to %d events" % (len(events)))
# get data in correct format
data = np.transpose(data) # make it [d x tau]
# 1: detrend
data = preproc.detrend(data)
# 2: bad-channel removal (as identifed in classifier training)
data = data[goodch, :, :]
# 3: apply spatial filter (as in classifier training)
data = preproc.spatialfilter(data, type=spatialfilter)
# 4 & 5: spectral filter (TODO: check fs matches!!)
data = preproc.fftfilter(data, 1, freqbands, fs)
# 6 : bad-trial removal
# 7: apply the classifier, get raw predictions
X2d = np.reshape(
data,
(-1,
data.shape[2])).T # sklearn needs data to be [nTrials x nFeatures]
fraw = classifier.predict(X2d)
# 8: map from fraw to event values (note probably not necessary here!)
#predictions = [ ivaluedict[round(i)] for i in fraw ]
# send the prediction events
for i, f in enumerate(fraw):
print("%d) {%s}=%f(raw)\n" % (i, str(events[i]), f))
bufhelp.sendEvent("classifier.prediction", f)
## 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)
# set stimuli to visible
txthdl.set(visible=False)
[_.set(facecolor=bgColor,visible=True) for _ in hdls]
bufhelp.sendEvent('stimulus.training','start')
state=None
txthdl.set(visible=False)
## STARTING stimulus loop
for si,tgt in enumerate(tgtSeq):
sleep(intertrialDuration)
# show the baseline
hdls[-1].set(facecolor=fixColor) # fixation cross red
drawnow()
bufhelp.sendEvent('stimulus.baseline','start')
sleep(baselineDuration)
bufhelp.sendEvent('stimulus.baseline','end')
#show the target
elif e.value == "training":
print("Training classifier")
data = preproc.detrend(data)
data, badch = preproc.badchannelremoval(data)
data = preproc.spatialfilter(data)
data = preproc.spectralfilter(data, (1, 10, 15, 25),
bufhelp.fSample)
data, events, badtrials = preproc.badtrailremoval(data, events)
mapping = {
('stimulus.hybrid', 'left'): -1,
('stimulus.hybrid', 'right'): 1
}
classifier = linear.fit(data, events, mapping)
bufhelp.update()
bufhelp.sendEvent("sigproc.training", "done")
elif e.value == "contfeedback":
print("Feedback phase")
while True:
test_data, test_events, stopevents = bufhelp.gatherdata(
"stimulus.hybrid",
trlen_ms,
"stimulus.hybrid",
milliseconds=True)
# if isinstance(stopevents, list):
# if any(["stimulus.feedback" in x.type for x in stopevents]):
# break
# else:
# if "stimulus.sequence" in stopevents.type:
data = np.reshape(data, (1,-1)) # Data now, is an bunch of array containing the power spectrum of every channel in the selected frequencies (#ch, #freq), in this line all those arrays become one of size (1, #ch * #freq)
prediction_right = classifier_right.predict(data) # The freq informations is now passed to the classifier, each one yielding a float value
prediction_left = classifier_left.predict(data)
pred = [prediction_left, prediction_right] # The predictions are put into an array, for debugging reasons, this isn't what will be passed
prediction = [np.round(prediction_left).astype(int), np.round(prediction_right).astype(int)] # In this array the predictions are rounded, as the expected values are 0,1 for each classifier but the outputs are float, they need to be rounded
# In the following lunes, the conversion is made from a the array to a single int value, easier to pass as an event value
# 0 - left [1, 0] 1- right [0, 1] 2 - both [1, 1] 3 - none [0, 0]
if prediction == [0, 0]: # None
prediction_int = 3
if prediction == [1, 0]: # Left Hand
prediction_int = 0
if prediction == [0, 1]: # Right Hand
prediction_int = 1
if prediction == [1, 1]: # Both Hands
prediction_int = 2
if verbose: print(pred, prediction) # Information in terminal, verbose option, for debugging purposes
bufhelp.sendEvent('classifier.prediction', str(prediction_int)) # After the classifier has done its job, send an event with ethe expected result as value
# This iteration has ended, now the classifier goes back to the biggining of the loop and waits for more events
hdls=initGrid(symbols)
[ _.set(visible=False) for _ in hdls] # make all invisible
## init connection to the buffer
ftc,hdr=bufhelp.connect();
#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()
bufhelp.sendEvent('stimulus.training','start');
## STARTING stimulus loop
for ti,tgt in enumerate(tgtSeq):
sleep(interSeqDuration)
#show the target/cue
print("%d) tgt=%d :"%(ti,tgt))
[_.set(color=bgColor,visible=True) for _ in hdls]
hdls[tgt].set(color=tgtColor)
drawnow()
bufhelp.sendEvent('stimulus.target',tgt)
bufhelp.sendEvent('stimulus.trial','start')
sleep(cueDuration)
# reset the display
rect = patches.Rectangle((0-stimRadius/4,0-stimRadius/4),stimRadius/2,stimRadius/2,facecolor=bgColor)
hhi =ax.add_patch(rect)
hdls.insert(nSymbs,hhi)
[ _.set(visible=False) for _ in hdls] # make all invisible
## 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()
# 7: apply the classifier, get raw predictions
X2d = np.reshape(
data,
(data.shape[0], data.shape[1] *
data.shape[2])) # sklearn needs data to be [nTrials x nFeatures]
fraw = classifier.predict(
scaler.transform(X2d)) # normalize the features
# 8: map from fraw to event values
# send the prediction events
for i, f in enumerate(fraw):
if f < treshold:
new_p = 0
else:
new_p = 1
print("%d) %f(raw)\n" % (i, new_p))
bufhelp.sendEvent("errp.prediction", new_p)
else:
# DEBUG
if verbose: print('Collecting data...')
data, events, stopevents, pending = bufhelp.gatherdata(
"errp.trigger", recording_lenght, [], milliseconds=True)
# get all event type labels
event_types = [e.type for e in events]
# Send artificial prediction
bufhelp.sendEvent("errp.prediction", debug_predictions[0])
print(debug_predictions[0])
debug_predictions = debug_predictions[1:]