def run(cfg, state=mp.Value('i', 1), queue=None):
"""
Online protocol for Alpha/Theta neurofeedback.
"""
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol runs if state equals to 1
if not state.value:
sys.exit(-1)
#----------------------------------------------------------------------
# LSL stream connection
#----------------------------------------------------------------------
# chooose amp
amp_name, amp_serial = find_lsl_stream(cfg, state)
# Connect to lsl stream
sr = connect_lsl_stream(cfg, amp_name, amp_serial)
# Get sampling rate
sfreq = sr.get_sample_rate()
# Get trigger channel
trg_ch = sr.get_trigger_channel()
#----------------------------------------------------------------------
# Main
#----------------------------------------------------------------------
global_timer = qc.Timer(autoreset=False)
internal_timer = qc.Timer(autoreset=True)
while state.value == 1 and global_timer.sec() < cfg.GLOBAL_TIME:
#----------------------------------------------------------------------
# Data acquisition
#----------------------------------------------------------------------
sr.acquire()
window, tslist = sr.get_window() # window = [samples x channels]
window = window.T # window = [channels x samples]
# Check if proper real-time acquisition
tsnew = np.where(np.array(tslist) > last_ts)[0]
if len(tsnew) == 0:
logger.warning('There seems to be delay in receiving data.')
time.sleep(1)
continue
#----------------------------------------------------------------------
# ADD YOUR CODE HERE
#----------------------------------------------------------------------
last_ts = tslist[-1]
internal_timer.sleep_atleast(cfg.TIMER_SLEEP)
def run(cfg, state=mp.Value('i', 1), queue=None):
"""
Training protocol for Alpha/Theta neurofeedback.
"""
redirect_stdout_to_queue(logger, queue, 'INFO')
# add tdef object
cfg.tdef = trigger_def(cfg.TRIGGER_FILE)
# Extract features
if not state.value:
sys.exit(-1)
freqs = np.arange(cfg.FEATURES['PSD']['fmin'], cfg.FEATURES['PSD']['fmax']+0.5, 1/cfg.FEATURES['PSD']['wlen'])
featdata = features.compute_features(cfg)
# Average the PSD over the windows
window_avg_psd = np.mean(np.squeeze(featdata['X_data']), 0)
# Alpha ref over the alpha band
alpha_ref = round(np.mean(window_avg_psd[freqs>=8]))
alpha_thr = round(alpha_ref - (0.5 * np.std(window_avg_psd[freqs>=8]) ))
# Theta ref over Theta band
theta_ref = round(np.mean(window_avg_psd[freqs<8]))
theta_thr = round(theta_ref - (0.5 * np.std(window_avg_psd[freqs<8]) ))
logger.info('Theta ref = {}; alpha ref ={}' .format(theta_ref, alpha_ref))
logger.info('Theta thr = {}; alpha thr ={}' .format(theta_thr, alpha_thr))
def run(cfg, state=mp.Value('i', 1), queue=None, logger=logger):
'''
Main function used to run the offline protocol.
Parameters
----------
cfg : python.module
The loaded config module from the corresponding config_offline.py
queue : mp.Queue
If not None, redirect sys.stdout to GUI terminal
logger : logging.logger
The logger to use
'''
redirect_stdout_to_queue(logger, queue, 'INFO')
# Load the mapping from int to string for triggers events
cfg.tdef = TriggerDef(cfg.TRIGGER_FILE)
# Protocol start if equals to 1
if not state.value:
sys.exit(-1)
#-------------------------------------
# ADD YOUR CODE HERE
#-------------------------------------
# TO train a decoder, look at trainer_mi.py protocol
with state.get_lock():
state.value = 0
def run(cfg, state=mp.Value('i', 1), queue=None, logger=logger):
'''
Main function used to run the offline protocol.
Parameters
----------
cfg : python.module
The loaded config module from the corresponding config_offline.py
queue : mp.Queue
If not None, redirect sys.stdout to GUI terminal
logger : logging.logger
The logger to use
'''
# Use to redirect sys.stdout to GUI terminal if GUI usage
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol start if equals to 1
if not state.value:
sys.exit()
# Load the mapping from int to string for triggers events
cfg.tdef = TriggerDef(cfg.TRIGGER_FILE)
# Refresh rate
refresh_delay = 1.0 / cfg.REFRESH_RATE
# Trigger
trigger = Trigger(lpttype=cfg.TRIGGER_DEVICE, state=state)
if trigger.init(50) == False:
logger.error(
'\n** Error connecting to the trigger device. Use a mock trigger instead?'
)
input('Press Ctrl+C to stop or Enter to continue.')
trigger = Trigger(lpttype='FAKE')
trigger.init(50)
# timers
timer_refresh = Timer()
trial = 1
num_trials = cfg.TRIALS_NB
# start
while trial <= num_trials:
timer_refresh.sleep_atleast(refresh_delay)
timer_refresh.reset()
#-------------------------------------
# ADD YOUR CODE HERE
#-------------------------------------
with state.get_lock():
state.value = 0
def run(cfg, state=mp.Value('i', 1), queue=None):
"""
Online protocol for Alpha/Theta neurofeedback.
"""
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol runs if state equals to 1
if not state.value:
sys.exit(-1)
def run_gui(recordState,
protocolState,
record_dir,
recordLogger=logger,
amp_name=None,
amp_serial=None,
eeg_only=False,
queue=None):
redirect_stdout_to_queue(recordLogger, queue, 'INFO')
# configure LSL server name and device serial if available
if not amp_name:
amp_name, amp_serial = pu.search_lsl(recordState,
recordLogger,
ignore_markers=True)
recordLogger.info('\nOutput directory: %s' % (record_dir))
# spawn the recorder as a child process
recordLogger.info('\n>> Recording started.')
proc = mp.Process(target=record,
args=[
recordState, amp_name, amp_serial, record_dir,
eeg_only, recordLogger, queue
])
proc.start()
while not recordState.value:
pass
# Launching the protocol (shared variable)
with protocolState.get_lock():
protocolState.value = 1
# Continue recording until the shared variable changes to 0.
while recordState.value:
time.sleep(1)
recordLogger.info('(main) Waiting for recorder process to finish.')
proc.join(10)
if proc.is_alive():
recordLogger.error(
'Recorder process not finishing. Are you running from Spyder?')
recordLogger.error('Dropping into a shell')
qc.shell()
sys.stdout.flush()
recordLogger.info('Recording finished.')
def redirect_stdout(self):
"""
Create Queue and redirect sys.stdout to this queue.
Create thread that will listen on the other end of the queue, and send the text to the textedit_terminal.
"""
queue = mp.Queue()
self.thread = QThread()
self.my_receiver = MyReceiver(queue)
self.my_receiver.mysignal.connect(self.on_terminal_append)
self.my_receiver.moveToThread(self.thread)
self.thread.started.connect(self.my_receiver.run)
self.thread.start()
redirect_stdout_to_queue(logger, self.my_receiver.queue, 'INFO')
def __init__(self,
amp_name,
amp_serial,
state=mp.Value('i', 1),
queue=None):
super(Scope, self).__init__()
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
redirect_stdout_to_queue(logger, queue, 'INFO')
logger.info('Viewer launched')
self.amp_name = amp_name
self.amp_serial = amp_serial
self.state = state
self.init_scope()
def __init__(self,
amp_name,
amp_serial,
state=mp.Value('i', 1),
queue=None):
super(Scope, self).__init__()
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
self.setGeometry(100, 100,
self.geometry().width(),
self.geometry().height())
self.setFixedSize(self.geometry().width(), self.geometry().height())
redirect_stdout_to_queue(logger, queue, 'INFO')
logger.info('Viewer launched')
self.amp_name = amp_name
self.amp_serial = amp_serial
self.state = state
self.recordState = mp.Value('i', 0)
self.init_scope()
def run(cfg,
state=mp.Value('i', 1),
queue=None,
interactive=False,
cv_file=None,
feat_file=None,
logger=logger):
redirect_stdout_to_queue(logger, queue, 'INFO')
# add tdef object
cfg.tdef = trigger_def(cfg.TRIGGER_FILE)
# Extract features
if not state.value:
sys.exit(-1)
featdata = features.compute_features(cfg)
# Find optimal threshold for TPR balancing
#balance_tpr(cfg, featdata)
# Perform cross validation
if not state.value:
sys.exit(-1)
if cfg.CV_PERFORM[cfg.CV_PERFORM['selected']] is not None:
cross_validate(cfg, featdata, cv_file=cv_file)
# Train a decoder
if not state.value:
sys.exit(-1)
if cfg.EXPORT_CLS is True:
train_decoder(cfg, featdata, feat_file=feat_file)
with state.get_lock():
state.value = 0
def record(recordState,
amp_name,
amp_serial,
record_dir,
eeg_only,
recordLogger=logger,
queue=None):
redirect_stdout_to_queue(recordLogger, queue, 'INFO')
# set data file name
timestamp = time.strftime('%Y%m%d-%H%M%S', time.localtime())
pcl_file = "%s/%s-raw.pcl" % (record_dir, timestamp)
eve_file = '%s/%s-eve.txt' % (record_dir, timestamp)
recordLogger.info('>> Output file: %s' % (pcl_file))
# test writability
try:
qc.make_dirs(record_dir)
open(
pcl_file,
'w').write('The data will written when the recording is finished.')
except:
raise RuntimeError('Problem writing to %s. Check permission.' %
pcl_file)
# start a server for sending out data pcl_file when software trigger is used
outlet = start_server('StreamRecorderInfo', channel_format='string',\
source_id=eve_file, stype='Markers')
# connect to EEG stream server
sr = StreamReceiver(buffer_size=0,
amp_name=amp_name,
amp_serial=amp_serial,
eeg_only=eeg_only)
# start recording
recordLogger.info('\n>> Recording started (PID %d).' % os.getpid())
with recordState.get_lock():
recordState.value = 1
tm = qc.Timer(autoreset=True)
next_sec = 1
while recordState.value == 1:
sr.acquire()
if sr.get_buflen() > next_sec:
duration = str(datetime.timedelta(seconds=int(sr.get_buflen())))
recordLogger.info('RECORDING %s' % duration)
next_sec += 1
tm.sleep_atleast(0.001)
# record stop
recordLogger.info('>> Stop requested. Copying buffer')
buffers, times = sr.get_buffer()
signals = buffers
events = None
# channels = total channels from amp, including trigger channel
data = {
'signals': signals,
'timestamps': times,
'events': events,
'sample_rate': sr.get_sample_rate(),
'channels': sr.get_num_channels(),
'ch_names': sr.get_channel_names(),
'lsl_time_offset': sr.lsl_time_offset
}
recordLogger.info('Saving raw data ...')
qc.save_obj(pcl_file, data)
recordLogger.info('Saved to %s\n' % pcl_file)
# automatically convert to fif and use event file if it exists (software trigger)
if os.path.exists(eve_file):
recordLogger.info('Found matching event file, adding events.')
else:
eve_file = None
recordLogger.info('Converting raw file into fif.')
pcl2fif(pcl_file, external_event=eve_file)
def run(cfg, state=mp.Value('i', 1), queue=None):
def confusion_matrix(Y_true, Y_pred, label_len=6):
"""
Generate confusion matrix in a string format
Parameters
----------
Y_true : list
The true labels
Y_pred : list
The test labels
label_len : int
The maximum label text length displayed (minimum length: 6)
Returns
-------
cfmat : str
The confusion matrix in str format (X-axis: prediction, -axis: ground truth)
acc : float
The accuracy
"""
import numpy as np
from sklearn.metrics import confusion_matrix as sk_confusion_matrix
# find labels
if type(Y_true) == np.ndarray:
Y_labels = np.unique(Y_true)
else:
Y_labels = list(set(Y_true))
# Check the provided label name length
if label_len < 6:
label_len = 6
logger.warning('label_len < 6. Setting to 6.')
label_tpl = '%' + '-%ds' % label_len
col_tpl = '%' + '-%d.2f' % label_len
# sanity check
if len(Y_pred) > len(Y_true):
raise RuntimeError('Y_pred has more items than Y_true')
elif len(Y_pred) < len(Y_true):
Y_true = Y_true[:len(Y_pred)]
cm = sk_confusion_matrix(Y_true, Y_pred, Y_labels)
# compute confusion matrix
cm_rate = cm.copy().astype('float')
cm_sum = np.sum(cm, axis=1)
# Fill confusion string
for r, s in zip(cm_rate, cm_sum):
if s > 0:
r /= s
cm_txt = label_tpl % 'gt\dt'
for l in Y_labels:
cm_txt += label_tpl % str(l)[:label_len]
cm_txt += '\n'
for l, r in zip(Y_labels, cm_rate):
cm_txt += label_tpl % str(l)[:label_len]
for c in r:
cm_txt += col_tpl % c
cm_txt += '\n'
# compute accuracy
correct = 0.0
for c in range(cm.shape[0]):
correct += cm[c][c]
cm_sum = cm.sum()
if cm_sum > 0:
acc = correct / cm.sum()
else:
acc = 0.0
return cm_txt, acc
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol runs if state equals to 1
if not state.value:
sys.exit(-1)
if cfg.FAKE_CLS is None:
# chooose amp
if cfg.AMP_NAME is None:
amp_name = search_lsl(ignore_markers=True, state=state)
else:
amp_name = cfg.AMP_NAME
fake_dirs = None
else:
amp_name = None
fake_dirs = [v for (k, v) in cfg.DIRECTIONS]
# events and triggers
tdef = TriggerDef(cfg.TRIGGER_FILE)
#if cfg.TRIGGER_DEVICE is None:
# input('\n** Warning: No trigger device set. Press Ctrl+C to stop or Enter to continue.')
trigger = Trigger(cfg.TRIGGER_DEVICE, state)
if trigger.init(50) == False:
logger.error(
'Cannot connect to USB2LPT device. Use a mock trigger instead?')
input('Press Ctrl+C to stop or Enter to continue.')
trigger = Trigger('FAKE', state)
trigger.init(50)
# For adaptive (need to share the actual true label accross process)
label = mp.Value('i', 0)
# init classification
decoder = BCIDecoderDaemon(amp_name, cfg.DECODER_FILE, buffer_size=1.0, fake=(cfg.FAKE_CLS is not None), fake_dirs=fake_dirs, \
parallel=cfg.PARALLEL_DECODING[cfg.PARALLEL_DECODING['selected']], alpha_new=cfg.PROB_ALPHA_NEW, label=label)
# OLD: requires trigger values to be always defined
#labels = [tdef.by_value[x] for x in decoder.get_labels()]
# NEW: events can be mapped into integers:
labels = []
dirdata = set([d[1] for d in cfg.DIRECTIONS])
for x in decoder.get_labels():
if x not in dirdata:
labels.append(tdef.by_value[x])
else:
labels.append(x)
# map class labels to bar directions
bar_def = {label: str(dir) for dir, label in cfg.DIRECTIONS}
bar_dirs = [bar_def[l] for l in labels]
dir_seq = []
for x in range(cfg.TRIALS_EACH):
dir_seq.extend(bar_dirs)
logger.info('Initializing decoder.')
while decoder.is_running() == 0:
time.sleep(0.01)
# bar visual object
if cfg.FEEDBACK_TYPE == 'BAR':
from neurodecode.protocols.viz_bars import BarVisual
visual = BarVisual(cfg.GLASS_USE,
screen_pos=cfg.SCREEN_POS,
screen_size=cfg.SCREEN_SIZE)
elif cfg.FEEDBACK_TYPE == 'BODY':
assert hasattr(cfg, 'FEEDBACK_IMAGE_PATH'
), 'FEEDBACK_IMAGE_PATH is undefined in your config.'
from neurodecode.protocols.viz_human import BodyVisual
visual = BodyVisual(cfg.FEEDBACK_IMAGE_PATH,
use_glass=cfg.GLASS_USE,
screen_pos=cfg.SCREEN_POS,
screen_size=cfg.SCREEN_SIZE)
visual.put_text('Waiting to start')
if cfg.LOG_PROBS:
logdir = io.parse_path(cfg.DECODER_FILE).dir
probs_logfile = time.strftime(logdir + "probs-%Y%m%d-%H%M%S.txt",
time.localtime())
else:
probs_logfile = None
feedback = Feedback(cfg, state, visual, tdef, trigger, probs_logfile)
# If adaptive classifier
if cfg.ADAPTIVE[cfg.ADAPTIVE['selected']]:
nb_runs = cfg.ADAPTIVE[cfg.ADAPTIVE['selected']][0]
adaptive = True
else:
nb_runs = 1
adaptive = False
run = 1
while run <= nb_runs:
if cfg.TRIALS_RANDOMIZE:
random.shuffle(dir_seq)
else:
dir_seq = [d[0] for d in cfg.DIRECTIONS] * cfg.TRIALS_EACH
num_trials = len(dir_seq)
# For adaptive, retrain classifier
if run > 1:
# Allow to retrain classifier
with decoder.label.get_lock():
decoder.label.value = 1
# Wait that the retraining is done
while decoder.label.value == 1:
time.sleep(0.01)
feedback.viz.put_text('Press any key')
feedback.viz.update()
cv2.waitKeyEx()
feedback.viz.fill()
# start
trial = 1
dir_detected = []
prob_history = {c: [] for c in bar_dirs}
while trial <= num_trials:
if cfg.SHOW_TRIALS:
title_text = 'Trial %d / %d' % (trial, num_trials)
else:
title_text = 'Ready'
true_label = dir_seq[trial - 1]
# profiling feedback
#import cProfile
#pr = cProfile.Profile()
#pr.enable()
result = feedback.classify(decoder,
true_label,
title_text,
bar_dirs,
prob_history=prob_history,
adaptive=adaptive)
#pr.disable()
#pr.print_stats(sort='time')
if result is None:
decoder.stop()
return
else:
pred_label = result
dir_detected.append(pred_label)
if cfg.WITH_REX is True and pred_label == true_label:
# if cfg.WITH_REX is True:
if pred_label == 'U':
rex_dir = 'N'
elif pred_label == 'L':
rex_dir = 'W'
elif pred_label == 'R':
rex_dir = 'E'
elif pred_label == 'D':
rex_dir = 'S'
else:
logger.warning('Rex cannot execute undefined action %s' %
pred_label)
rex_dir = None
if rex_dir is not None:
visual.move(pred_label, 100, overlay=False, barcolor='B')
visual.update()
logger.info('Executing Rex action %s' % rex_dir)
os.system(
'%s/Rex/RexControlSimple.exe %s %s' %
(os.environ['NEUROD_ROOT'], cfg.REX_COMPORT, rex_dir))
time.sleep(8)
if true_label == pred_label:
msg = 'Correct'
else:
msg = 'Wrong'
if cfg.TRIALS_RETRY is False or true_label == pred_label:
logger.info('Trial %d: %s (%s -> %s)' %
(trial, msg, true_label, pred_label))
trial += 1
if len(dir_detected) > 0:
# write performance and log results
fdir = io.parse_path(cfg.DECODER_FILE).dir
logfile = time.strftime(fdir + "/online-%Y%m%d-%H%M%S.txt",
time.localtime())
with open(logfile, 'w') as fout:
fout.write('Ground-truth,Prediction\n')
for gt, dt in zip(dir_seq, dir_detected):
fout.write('%s,%s\n' % (gt, dt))
cfmat, acc = confusion_matrix(dir_seq, dir_detected)
fout.write('\nAccuracy %.3f\nConfusion matrix\n' % acc)
fout.write(cfmat)
logger.info('Log exported to %s' % logfile)
print('\nAccuracy %.3f\nConfusion matrix\n' % acc)
print(cfmat)
run += 1
visual.finish()
with state.get_lock():
state.value = 0
if decoder.is_running():
decoder.stop()
'''
# automatic thresholding
if prob_history and len(bar_dirs) == 2:
total = sum(len(prob_history[c]) for c in prob_history)
fout = open(probs_logfile, 'a')
msg = 'Automatic threshold optimization.\n'
max_acc = 0
max_bias = 0
for bias in np.arange(-0.99, 1.00, 0.01):
corrects = 0
for p in prob_history[bar_dirs[0]]:
p_biased = (p + bias) / (bias + 1) # new sum = (p+bias) + (1-p) = bias+1
if p_biased >= 0.5:
corrects += 1
for p in prob_history[bar_dirs[1]]:
p_biased = (p + bias) / (bias + 1) # new sum = (p+bias) + (1-p) = bias+1
if p_biased < 0.5:
corrects += 1
acc = corrects / total
msg += '%s%.2f: %.3f\n' % (bar_dirs[0], bias, acc)
if acc > max_acc:
max_acc = acc
max_bias = bias
msg += 'Max acc = %.3f at bias %.2f\n' % (max_acc, max_bias)
fout.write(msg)
fout.close()
print(msg)
'''
logger.info('Finished.')
def run(cfg, state=mp.Value('i', 1), queue=None):
'''
Main function used to run the online protocol.
Parameters
----------
cfg : python.module
The loaded config module from the corresponding config_offline.py
queue : mp.Queue
If not None, redirect sys.stdout to GUI terminal
logger : logging.logger
The logger to use
'''
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol runs if state equals to 1
if not state.value:
sys.exit(-1)
# events and triggers
cfg.tdef = TriggerDef(cfg.TRIGGER_FILE)
# To send trigger events
trigger = Trigger(cfg.TRIGGER_DEVICE, state)
if trigger.init(50) == False:
logger.error(
'Cannot connect to trigger device. Use a mock trigger instead?')
input('Press Ctrl+C to stop or Enter to continue.')
trigger = Trigger('FAKE', state)
trigger.init(50)
# Instance a stream receiver
sr = StreamReceiver(bufsize=1,
winsize=0.5,
stream_name=None,
eeg_only=True)
# Timer for acquisition rate, here 20 Hz
tm = Timer(autoreset=True)
# Refresh rate
refresh_delay = 1.0 / cfg.REFRESH_RATE
while True:
# Acquire data from all the connected LSL streams by filling each associated buffers.
sr.acquire()
# Extract the latest window from the buffer of the chosen stream.
window, tslist = sr.get_window(
) # window = [samples x channels], tslist = [samples]
#-------------------------------------
# ADD YOUR CODE HERE
#-------------------------------------
# To run a trained BCI decoder, look at online_mi.py protocol
tm.sleep_atleast(refresh_delay)
with state.get_lock():
state.value = 0
logger.info('Finished.')
def run(cfg, state=mp.Value('i', 1), queue=None):
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol start if equals to 1
if not state.value:
sys.exit()
refresh_delay = 1.0 / cfg.REFRESH_RATE
cfg.tdef = trigger_def(cfg.TRIGGER_FILE)
# visualizer
keys = {
'left': 81,
'right': 83,
'up': 82,
'down': 84,
'pgup': 85,
'pgdn': 86,
'home': 80,
'end': 87,
'space': 32,
'esc': 27,
',': 44,
'.': 46,
's': 115,
'c': 99,
'[': 91,
']': 93,
'1': 49,
'!': 33,
'2': 50,
'@': 64,
'3': 51,
'#': 35
}
color = dict(G=(20, 140, 0),
B=(210, 0, 0),
R=(0, 50, 200),
Y=(0, 215, 235),
K=(0, 0, 0),
w=(200, 200, 200))
dir_sequence = []
for x in range(cfg.TRIALS_EACH):
dir_sequence.extend(cfg.DIRECTIONS)
random.shuffle(dir_sequence)
num_trials = len(cfg.DIRECTIONS) * cfg.TRIALS_EACH
event = 'start'
trial = 1
# Hardware trigger
if cfg.TRIGGER_DEVICE is None:
logger.warning(
'No trigger device set. Press Ctrl+C to stop or Enter to continue.'
)
#input()
trigger = pyLptControl.Trigger(state, cfg.TRIGGER_DEVICE)
if trigger.init(50) == False:
logger.error(
'\n** Error connecting to USB2LPT device. Use a mock trigger instead?'
)
input('Press Ctrl+C to stop or Enter to continue.')
trigger = pyLptControl.MockTrigger()
trigger.init(50)
# timers
timer_trigger = qc.Timer()
timer_dir = qc.Timer()
timer_refresh = qc.Timer()
t_dir = cfg.TIMINGS['DIR'] + random.uniform(-cfg.TIMINGS['DIR_RANDOMIZE'],
cfg.TIMINGS['DIR_RANDOMIZE'])
t_dir_ready = cfg.TIMINGS['READY'] + random.uniform(
-cfg.TIMINGS['READY_RANDOMIZE'], cfg.TIMINGS['READY_RANDOMIZE'])
bar = BarVisual(cfg.GLASS_USE,
screen_pos=cfg.SCREEN_POS,
screen_size=cfg.SCREEN_SIZE)
bar.fill()
bar.glass_draw_cue()
# start
while trial <= num_trials:
timer_refresh.sleep_atleast(refresh_delay)
timer_refresh.reset()
# segment= { 'cue':(s,e), 'dir':(s,e), 'label':0-4 } (zero-based)
if event == 'start' and timer_trigger.sec() > cfg.TIMINGS['INIT']:
event = 'gap_s'
bar.fill()
timer_trigger.reset()
trigger.signal(cfg.tdef.INIT)
elif event == 'gap_s':
if cfg.TRIAL_PAUSE:
bar.put_text('Press any key')
bar.update()
key = cv2.waitKey()
if key == keys['esc'] or not state.value:
break
bar.fill()
bar.put_text('Trial %d / %d' % (trial, num_trials))
event = 'gap'
timer_trigger.reset()
elif event == 'gap' and timer_trigger.sec() > cfg.TIMINGS['GAP']:
event = 'cue'
bar.fill()
bar.draw_cue()
trigger.signal(cfg.tdef.CUE)
timer_trigger.reset()
elif event == 'cue' and timer_trigger.sec() > cfg.TIMINGS['CUE']:
event = 'dir_r'
dir = dir_sequence[trial - 1]
if dir == 'L': # left
bar.move('L', 100, overlay=True)
trigger.signal(cfg.tdef.LEFT_READY)
elif dir == 'R': # right
bar.move('R', 100, overlay=True)
trigger.signal(cfg.tdef.RIGHT_READY)
elif dir == 'U': # up
bar.move('U', 100, overlay=True)
trigger.signal(cfg.tdef.UP_READY)
elif dir == 'D': # down
bar.move('D', 100, overlay=True)
trigger.signal(cfg.tdef.DOWN_READY)
elif dir == 'B': # both hands
bar.move('L', 100, overlay=True)
bar.move('R', 100, overlay=True)
trigger.signal(cfg.tdef.BOTH_READY)
else:
raise RuntimeError('Unknown direction %d' % dir)
timer_trigger.reset()
elif event == 'dir_r' and timer_trigger.sec() > t_dir_ready:
bar.fill()
bar.draw_cue()
event = 'dir'
timer_trigger.reset()
timer_dir.reset()
if dir == 'L': # left
trigger.signal(cfg.tdef.LEFT_GO)
elif dir == 'R': # right
trigger.signal(cfg.tdef.RIGHT_GO)
elif dir == 'U': # up
trigger.signal(cfg.tdef.UP_GO)
elif dir == 'D': # down
trigger.signal(cfg.tdef.DOWN_GO)
elif dir == 'B': # both
trigger.signal(cfg.tdef.BOTH_GO)
else:
raise RuntimeError('Unknown direction %d' % dir)
elif event == 'dir' and timer_trigger.sec() > t_dir:
event = 'gap_s'
bar.fill()
trial += 1
logger.info('trial ' + str(trial - 1) + ' done')
trigger.signal(cfg.tdef.BLANK)
timer_trigger.reset()
t_dir = cfg.TIMINGS['DIR'] + random.uniform(
-cfg.TIMINGS['DIR_RANDOMIZE'], cfg.TIMINGS['DIR_RANDOMIZE'])
t_dir_ready = cfg.TIMINGS['READY'] + random.uniform(
-cfg.TIMINGS['READY_RANDOMIZE'],
cfg.TIMINGS['READY_RANDOMIZE'])
# protocol
if event == 'dir':
dx = min(100, int(100.0 * timer_dir.sec() / t_dir) + 1)
if dir == 'L': # L
bar.move('L', dx, overlay=True)
elif dir == 'R': # R
bar.move('R', dx, overlay=True)
elif dir == 'U': # U
bar.move('U', dx, overlay=True)
elif dir == 'D': # D
bar.move('D', dx, overlay=True)
elif dir == 'B': # Both
bar.move('L', dx, overlay=True)
bar.move('R', dx, overlay=True)
# wait for start
if event == 'start':
bar.put_text('Waiting to start')
bar.update()
key = 0xFF & cv2.waitKey(1)
if key == keys['esc'] or not state.value:
break
bar.finish()
with state.get_lock():
state.value = 0
def run(cfg, state=mp.Value('i', 1), queue=None):
"""
Offline protocol
"""
# visualizer
keys = {
'left': 81,
'right': 83,
'up': 82,
'down': 84,
'pgup': 85,
'pgdn': 86,
'home': 80,
'end': 87,
'space': 32,
'esc': 27,
',': 44,
'.': 46,
's': 115,
'c': 99,
'[': 91,
']': 93,
'1': 49,
'!': 33,
'2': 50,
'@': 64,
'3': 51,
'#': 35
}
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol runs if state equals to 1
if not state.value:
sys.exit(-1)
global_timer = qc.Timer(autoreset=False)
# Init trigger communication
cfg.tdef = trigger_def(cfg.TRIGGER_FILE)
trigger = pyLptControl.Trigger(state, cfg.TRIGGER_DEVICE)
if trigger.init(50) == False:
logger.error('\n** Error connecting to trigger device.')
raise RuntimeError
# Preload the starting voice
pgmixer.init()
pgmixer.music.load(cfg.START_VOICE)
# Init feedback
viz = BarVisual(cfg.GLASS_USE,
screen_pos=cfg.SCREEN_POS,
screen_size=cfg.SCREEN_SIZE)
viz.fill()
viz.put_text('Close your eyes and relax')
viz.update()
# PLay the start voice
pgmixer.music.play()
# Wait a key press
key = 0xFF & cv2.waitKey(0)
if key == keys['esc'] or not state.value:
sys.exit(-1)
viz.fill()
viz.put_text('Recording in progress')
viz.update()
#----------------------------------------------------------------------
# Main
#----------------------------------------------------------------------
trigger.signal(cfg.tdef.INIT)
while state.value == 1 and global_timer.sec() < cfg.GLOBAL_TIME:
key = cv2.waitKey(1)
if key == keys['esc']:
with state.get_lock():
state.value = 0
trigger.signal(cfg.tdef.END)
# Remove the text
viz.fill()
viz.put_text('Recording is finished')
viz.update()
# Ending voice
pgmixer.music.load(cfg.END_VOICE)
pgmixer.music.play()
time.sleep(5)
# Close cv2 window
viz.finish()
def run(cfg, state=mp.Value('i', 1), queue=None):
redirect_stdout_to_queue(logger, queue, 'INFO')
# Wait the recording to start (GUI)
while state.value == 2: # 0: stop, 1:start, 2:wait
pass
# Protocol runs if state equals to 1
if not state.value:
sys.exit(-1)
if cfg.FAKE_CLS is None:
# chooose amp
if cfg.AMP_NAME is None and cfg.AMP_SERIAL is None:
amp_name, amp_serial = pu.search_lsl(state, ignore_markers=True)
else:
amp_name = cfg.AMP_NAME
amp_serial = cfg.AMP_SERIAL
fake_dirs = None
else:
amp_name = None
amp_serial = None
fake_dirs = [v for (k, v) in cfg.DIRECTIONS]
# events and triggers
tdef = trigger_def(cfg.TRIGGER_FILE)
#if cfg.TRIGGER_DEVICE is None:
# input('\n** Warning: No trigger device set. Press Ctrl+C to stop or Enter to continue.')
trigger = pyLptControl.Trigger(state, cfg.TRIGGER_DEVICE)
if trigger.init(50) == False:
logger.error(
'Cannot connect to USB2LPT device. Use a mock trigger instead?')
input('Press Ctrl+C to stop or Enter to continue.')
trigger = pyLptControl.MockTrigger()
trigger.init(50)
# init classification
decoder = BCIDecoderDaemon(
cfg.DECODER_FILE,
buffer_size=1.0,
fake=(cfg.FAKE_CLS is not None),
amp_name=amp_name,
amp_serial=amp_serial,
fake_dirs=fake_dirs,
parallel=cfg.PARALLEL_DECODING[cfg.PARALLEL_DECODING['selected']],
alpha_new=cfg.PROB_ALPHA_NEW)
# OLD: requires trigger values to be always defined
#labels = [tdef.by_value[x] for x in decoder.get_labels()]
# NEW: events can be mapped into integers:
labels = []
dirdata = set([d[1] for d in cfg.DIRECTIONS])
for x in decoder.get_labels():
if x not in dirdata:
labels.append(tdef.by_value[x])
else:
labels.append(x)
# map class labels to bar directions
bar_def = {label: str(dir) for dir, label in cfg.DIRECTIONS}
bar_dirs = [bar_def[l] for l in labels]
dir_seq = []
for x in range(cfg.TRIALS_EACH):
dir_seq.extend(bar_dirs)
if cfg.TRIALS_RANDOMIZE:
random.shuffle(dir_seq)
else:
dir_seq = [d[0] for d in cfg.DIRECTIONS] * cfg.TRIALS_EACH
num_trials = len(dir_seq)
logger.info('Initializing decoder.')
while decoder.is_running() is 0:
time.sleep(0.01)
# bar visual object
if cfg.FEEDBACK_TYPE == 'BAR':
from neurodecode.protocols.viz_bars import BarVisual
visual = BarVisual(cfg.GLASS_USE,
screen_pos=cfg.SCREEN_POS,
screen_size=cfg.SCREEN_SIZE)
if cfg.FEEDBACK_TYPE == 'COLORS':
from neurodecode.protocols.viz_colors import ColorVisual
visual = ColorVisual(cfg.GLASS_USE,
screen_pos=cfg.SCREEN_POS,
screen_size=cfg.SCREEN_SIZE)
elif cfg.FEEDBACK_TYPE == 'BODY':
assert hasattr(cfg, 'FEEDBACK_IMAGE_PATH'
), 'FEEDBACK_IMAGE_PATH is undefined in your config.'
from neurodecode.protocols.viz_human import BodyVisual
visual = BodyVisual(cfg.FEEDBACK_IMAGE_PATH,
use_glass=cfg.GLASS_USE,
screen_pos=cfg.SCREEN_POS,
screen_size=cfg.SCREEN_SIZE)
visual.put_text('Waiting to start')
if cfg.LOG_PROBS:
logdir = qc.parse_path_list(cfg.DECODER_FILE)[0]
probs_logfile = time.strftime(logdir + "probs-%Y%m%d-%H%M%S.txt",
time.localtime())
else:
probs_logfile = None
feedback = Feedback(cfg, state, visual, tdef, trigger, probs_logfile)
# start
trial = 1
dir_detected = []
prob_history = {c: [] for c in bar_dirs}
while trial <= num_trials:
if cfg.SHOW_TRIALS:
title_text = 'Trial %d / %d' % (trial, num_trials)
else:
title_text = 'Ready'
true_label = dir_seq[trial - 1]
# profiling feedback
#import cProfile
#pr = cProfile.Profile()
#pr.enable()
result = feedback.classify(decoder,
true_label,
title_text,
bar_dirs,
prob_history=prob_history)
#pr.disable()
#pr.print_stats(sort='time')
if result is None:
break
else:
pred_label = result
dir_detected.append(pred_label)
if cfg.WITH_REX is True and pred_label == true_label:
# if cfg.WITH_REX is True:
if pred_label == 'U':
rex_dir = 'N'
elif pred_label == 'L':
rex_dir = 'W'
elif pred_label == 'R':
rex_dir = 'E'
elif pred_label == 'D':
rex_dir = 'S'
else:
logger.warning('Rex cannot execute undefined action %s' %
pred_label)
rex_dir = None
if rex_dir is not None:
visual.move(pred_label, 100, overlay=False, barcolor='B')
visual.update()
logger.info('Executing Rex action %s' % rex_dir)
os.system('%s/Rex/RexControlSimple.exe %s %s' %
(pycnbi.ROOT, cfg.REX_COMPORT, rex_dir))
time.sleep(8)
if true_label == pred_label:
msg = 'Correct'
else:
msg = 'Wrong'
if cfg.TRIALS_RETRY is False or true_label == pred_label:
logger.info('Trial %d: %s (%s -> %s)' %
(trial, msg, true_label, pred_label))
trial += 1
if len(dir_detected) > 0:
# write performance and log results
fdir, _, _ = qc.parse_path_list(cfg.DECODER_FILE)
logfile = time.strftime(fdir + "/online-%Y%m%d-%H%M%S.txt",
time.localtime())
with open(logfile, 'w') as fout:
fout.write('Ground-truth,Prediction\n')
for gt, dt in zip(dir_seq, dir_detected):
fout.write('%s,%s\n' % (gt, dt))
cfmat, acc = qc.confusion_matrix(dir_seq, dir_detected)
fout.write('\nAccuracy %.3f\nConfusion matrix\n' % acc)
fout.write(cfmat)
logger.info('Log exported to %s' % logfile)
print('\nAccuracy %.3f\nConfusion matrix\n' % acc)
print(cfmat)
visual.finish()
with state.get_lock():
state.value = 0
if decoder:
decoder.stop()
'''
# automatic thresholding
if prob_history and len(bar_dirs) == 2:
total = sum(len(prob_history[c]) for c in prob_history)
fout = open(probs_logfile, 'a')
msg = 'Automatic threshold optimization.\n'
max_acc = 0
max_bias = 0
for bias in np.arange(-0.99, 1.00, 0.01):
corrects = 0
for p in prob_history[bar_dirs[0]]:
p_biased = (p + bias) / (bias + 1) # new sum = (p+bias) + (1-p) = bias+1
if p_biased >= 0.5:
corrects += 1
for p in prob_history[bar_dirs[1]]:
p_biased = (p + bias) / (bias + 1) # new sum = (p+bias) + (1-p) = bias+1
if p_biased < 0.5:
corrects += 1
acc = corrects / total
msg += '%s%.2f: %.3f\n' % (bar_dirs[0], bias, acc)
if acc > max_acc:
max_acc = acc
max_bias = bias
msg += 'Max acc = %.3f at bias %.2f\n' % (max_acc, max_bias)
fout.write(msg)
fout.close()
print(msg)
'''
logger.info('Finished.')
