def record(state, amp_name, amp_serial, record_dir, eeg_only):
# set data file name
filename = time.strftime(record_dir + "/%Y%m%d-%H%M%S-raw.pcl",
time.localtime())
qc.print_c('>> Output file: %s' % (filename), 'W')
# test writability
try:
qc.make_dirs(record_dir)
open(
filename,
'w').write('The data will written when the recording is finished.')
except:
raise RuntimeError('Problem writing to %s. Check permission.' %
filename)
# start a server for sending out data filename when software trigger is used
outlet = start_server('StreamRecorderInfo', channel_format='string',\
source_id=filename, stype='Markers')
# connect to EEG stream server
sr = StreamReceiver(amp_name=amp_name,
amp_serial=amp_serial,
eeg_only=eeg_only)
# start recording
qc.print_c('\n>> Recording started (PID %d).' % os.getpid(), 'W')
qc.print_c('\n>> Press Enter to stop recording', 'G')
tm = qc.Timer(autoreset=True)
next_sec = 1
while state.value == 1:
sr.acquire()
if sr.get_buflen() > next_sec:
duration = str(datetime.timedelta(seconds=int(sr.get_buflen())))
print('RECORDING %s' % duration)
next_sec += 1
tm.sleep_atleast(0.01)
# record stop
qc.print_c('>> Stop requested. Copying buffer', 'G')
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()
}
qc.print_c('Saving raw data ...', 'W')
qc.save_obj(filename, data)
print('Saved to %s\n' % filename)
qc.print_c('Converting raw file into a fif format.', 'W')
pcl2fif(filename)
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())
qc.print_c('\n>> Press Enter to stop recording', 'G')
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 log_decoding_helper(state, event_queue, amp_name=None, amp_serial=None, autostop=False):
"""
Helper function to run StreamReceiver object in background
"""
logger.info('Event acquisition subprocess started.')
# wait for the start signal
while state.value == 0:
time.sleep(0.01)
# acquire event values and returns event times and event values
sr = StreamReceiver(buffer_size=0, amp_name=amp_name, amp_serial=amp_serial)
tm = qc.Timer(autoreset=True)
started = False
while state.value == 1:
chunk, ts_list = sr.acquire()
if autostop:
if started is True:
if len(ts_list) == 0:
state.value = 0
break
elif len(ts_list) > 0:
started = True
tm.sleep_atleast(0.001)
logger.info('Event acquisition subprocess finishing up ...')
buffers, times = sr.get_buffer()
events = buffers[:, 0] # first channel is the trigger channel
event_index = np.where(events != 0)[0]
event_times = times[event_index].reshape(-1).tolist()
event_values = events[event_index].tolist()
assert len(event_times) == len(event_values)
event_queue.put((event_times, event_values))
class Scope(QMainWindow):
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()
#
# Main init function
#
def init_scope(self):
# pg.setConfigOption('background', 'w')
# pg.setConfigOption('foreground', 'k')
self.init_config_file()
self.init_loop()
self.init_panel_GUI()
self.init_scope_GUI()
self.init_timer()
#
# Initializes config file
#
def init_config_file(self):
self.scope_settings = RawConfigParser(allow_no_value=True,
inline_comment_prefixes=('#',
';'))
if (len(sys.argv) == 1):
self.show_channel_names = 0
self.device_name = ""
else:
if (sys.argv[1].find("gtec") > -1):
self.device_name = "gtec"
self.show_channel_names = 1
elif (sys.argv[1].find("biosemi") > -1):
self.device_name = "biosemi"
self.show_channel_names = 1
elif (sys.argv[1].find("hiamp") > -1):
self.device_name = "hiamp"
self.show_channel_names = 1
else:
self.device_name = ""
self.show_channel_names = 0
# self.scope_settings.read(os.getenv("HOME") + "/.scope_settings.ini")
self.scope_settings.read(
str(path2_viewerFolder / '.scope_settings.ini'))
#
# Initialize control panel parameter
#
def init_panel_GUI(self):
self.show_TID_events = False
self.show_LPT_events = False
self.show_Key_events = False
# Event handler
self.ui.comboBox_scale.activated.connect(
self.onActivated_combobox_scale)
self.ui.spinBox_time.valueChanged.connect(
self.onValueChanged_spinbox_time)
self.ui.checkBox_car.stateChanged.connect(
self.onActivated_checkbox_car)
self.ui.checkBox_bandpass.stateChanged.connect(
self.onActivated_checkbox_bandpass)
self.ui.checkBox_showTID.stateChanged.connect(
self.onActivated_checkbox_TID)
self.ui.checkBox_showLPT.stateChanged.connect(
self.onActivated_checkbox_LPT)
self.ui.checkBox_showKey.stateChanged.connect(
self.onActivated_checkbox_Key)
self.ui.pushButton_bp.clicked.connect(self.onClicked_button_bp)
self.ui.pushButton_rec.clicked.connect(self.onClicked_button_rec)
self.ui.pushButton_stoprec.clicked.connect(
self.onClicked_button_stoprec)
self.ui.pushButton_stoprec.setEnabled(False)
self.ui.comboBox_scale.setCurrentIndex(4)
self.ui.checkBox_car.setChecked(
int(self.scope_settings.get("filtering", "apply_car_filter")))
self.ui.checkBox_bandpass.setChecked(
int(self.scope_settings.get("filtering", "apply_bandpass_filter")))
self.ui.checkBox_showTID.setChecked(
int(self.scope_settings.get("plot", "show_TID_events")))
self.ui.checkBox_showLPT.setChecked(
int(self.scope_settings.get("plot", "show_LPT_events")))
self.ui.checkBox_showKey.setChecked(
int(self.scope_settings.get("plot", "show_KEY_events")))
self.ui.statusBar.showMessage("[Not recording]")
self.channels_to_show_idx = []
idx = 0
for y in range(0, 4):
for x in range(0, NUM_X_CHANNELS):
if (idx < self.config['eeg_channels']):
# self.table_channels.item(x,y).setTextAlignment(QtCore.Qt.AlignCenter)
self.ui.table_channels.item(x, y).setSelected(True) # Qt5
#self.table_channels.setItemSelected(self.table_channels.item(x, y), True) # Qt4 only
self.channels_to_show_idx.append(idx)
else:
self.ui.table_channels.setItem(x, y,
QTableWidgetItem("N/A"))
self.ui.table_channels.item(x, y).setFlags(
QtCore.Qt.NoItemFlags)
self.ui.table_channels.item(x, y).setTextAlignment(
QtCore.Qt.AlignCenter)
idx += 1
self.ui.table_channels.verticalHeader().setStretchLastSection(True)
self.ui.table_channels.horizontalHeader().setStretchLastSection(True)
self.ui.table_channels.itemSelectionChanged.connect(
self.onSelectionChanged_table)
self.screen_width = 522
self.screen_height = 160
# self.setGeometry(100,100, self.screen_width, self.screen_height)
# self.setFixedSize(self.screen_width, self.screen_height)
self.setWindowTitle('EEG Scope Panel')
self.setFocusPolicy(QtCore.Qt.ClickFocus)
self.setFocus()
self.show()
#
# Initialize scope parameters
#
def init_scope_GUI(self):
self.bool_parser = {True: '1', False: '0'}
# PyQTGraph plot initialization
self.win = pg.GraphicsWindow()
self.win.setWindowTitle('EEG Scope')
self.win.setWindowFlags(QtCore.Qt.WindowMinimizeButtonHint)
self.win.keyPressEvent = self.keyPressEvent
self.win.show()
self.main_plot_handler = self.win.addPlot()
self.win.resize(1280, 800)
# Scales available in the GUI. If you change the options in the GUI
# you should change them here as well
self.scales_range = [1, 10, 25, 50, 100, 250, 500, 1000, 2500, 100000]
# Scale in uV
self.scale = int(self.scope_settings.get("plot", "scale_plot"))
# Time window to show in seconds
self.seconds_to_show = int(self.scope_settings.get(
"plot", "time_plot"))
# Y Tick labels. Use values from the config file.
self.channel_labels = []
values = []
''' For non-LSL systems having no channel names
for x in range(0, self.config['eeg_channels']):
if (self.show_channel_names):
self.channel_labels.append("(" + str(x + 1) + ") " +
self.scope_settings.get("internal",
"channel_names_" + self.device_name + str(
self.config['eeg_channels'])).split(', ')[x])
else:
self.channel_labels.append('CH ' + str(x + 1))
'''
ch_names = np.array(self.sr.get_channel_names())
self.channel_labels = ch_names[self.sr.get_eeg_channels()]
for x in range(0, len(self.channels_to_show_idx)):
values.append((-x * self.scale,
self.channel_labels[self.channels_to_show_idx[x]]))
values_axis = []
values_axis.append(values)
values_axis.append([])
# Update table labels with current names
idx = 0
for y in range(0, 4):
for x in range(0, NUM_X_CHANNELS):
if (idx < self.config['eeg_channels']):
self.ui.table_channels.item(x, y).setText(
self.channel_labels[idx])
idx += 1
# Plot initialization
self.main_plot_handler.getAxis('left').setTicks(values_axis)
self.main_plot_handler.setRange(
xRange=[0, self.seconds_to_show],
yRange=[
+1.5 * self.scale,
-0.5 * self.scale - self.scale * self.config['eeg_channels']
])
self.main_plot_handler.disableAutoRange()
self.main_plot_handler.showGrid(y=True)
self.main_plot_handler.setLabel(axis='left',
text='Scale (uV): ' + str(self.scale))
self.main_plot_handler.setLabel(axis='bottom', text='Time (s)')
# X axis
self.x_ticks = np.zeros(self.config['sf'] * self.seconds_to_show)
for x in range(0, self.config['sf'] * self.seconds_to_show):
self.x_ticks[x] = (x * 1) / float(self.config['sf'])
# Plotting colors. If channels > 16, colors will roll back to the beginning
self.colors = np.array([[255, 0, 0], [0, 255, 0], [0, 0, 255],
[255, 255, 0], [0, 255, 255], [255, 0, 255],
[128, 100, 100], [0, 128, 0], [0, 128, 128],
[128, 128, 0], [255, 128, 128], [128, 0, 128],
[128, 255, 0], [255, 128, 0], [0, 255, 128],
[128, 0, 255]])
# We want a lightweight scope, so we downsample the plotting to 64 Hz
self.subsampling_value = self.config['sf'] / 64
# EEG data for plotting
self.data_plot = np.zeros((self.config['sf'] * self.seconds_to_show,
self.config['eeg_channels']))
self.curve_eeg = []
for x in range(0, len(self.channels_to_show_idx)):
self.curve_eeg.append(
self.main_plot_handler.plot(
x=self.x_ticks,
y=self.data_plot[:, self.channels_to_show_idx[x]],
pen=pg.mkColor(self.colors[self.channels_to_show_idx[x] %
16, :])))
# self.curve_eeg[-1].setDownsampling(ds=self.subsampling_value, auto=False, method="mean")
# Events data
self.events_detected = []
self.events_curves = []
self.events_text = []
# CAR initialization
self.apply_car = int(
self.scope_settings.get("filtering", "apply_car_filter"))
self.matrix_car = np.zeros(
(self.config['eeg_channels'], self.config['eeg_channels']),
dtype=float)
self.matrix_car[:, :] = -1 / float(self.config['eeg_channels'])
np.fill_diagonal(self.matrix_car,
1 - (1 / float(self.config['eeg_channels'])))
# Laplacian initalization. TO BE DONE
self.matrix_lap = np.zeros(
(self.config['eeg_channels'], self.config['eeg_channels']),
dtype=float)
np.fill_diagonal(self.matrix_lap, 1)
'''
self.matrix_lap[2, 0] = -1
self.matrix_lap[0, 2] = -0.25
self.matrix_lap[0, 2] = -0.25
'''
# BP initialization
self.apply_bandpass = int(
self.scope_settings.get("filtering", "apply_bandpass_filter"))
if (self.apply_bandpass):
self.ui.doubleSpinBox_hp.setValue(
float(
self.scope_settings.get(
"filtering",
"bandpass_cutoff_frequency").split(' ')[0]))
self.ui.doubleSpinBox_lp.setValue(
float(
self.scope_settings.get(
"filtering",
"bandpass_cutoff_frequency").split(' ')[1]))
self.ui.doubleSpinBox_lp.setMinimum(0.1)
self.ui.doubleSpinBox_lp.setMaximum(self.sr.sample_rate / 2 - 0.1)
self.ui.doubleSpinBox_lp.setDecimals(1)
self.ui.doubleSpinBox_lp.setSingleStep(1)
self.ui.doubleSpinBox_hp.setMinimum(0.1)
self.ui.doubleSpinBox_hp.setMaximum(self.sr.sample_rate / 2 - 0.1)
self.ui.doubleSpinBox_hp.setDecimals(1)
self.ui.doubleSpinBox_hp.setSingleStep(1)
self.ui.pushButton_bp.click()
self.ui.checkBox_bandpass.setChecked(self.apply_car)
self.ui.checkBox_bandpass.setChecked(self.apply_bandpass)
self.update_title_scope()
# Help variables
self.show_help = 0
self.help = pg.TextItem(
"CNBI EEG Scope v0.3 \n" +
"----------------------------------------------------------------------------------\n"
+ "C: De/activate CAR Filter\n" +
"B: De/activate Bandpass Filter (with current settings)\n" +
"T: Show/hide TiD events\n" + "L: Show/hide LPT events\n" +
"K: Show/hide Key events. If not shown, they are NOT recorded!\n" +
"0-9: Add a user-specific Key event. Do not forget to write down why you marked it.\n"
+
"Up, down arrow keys: Increase/decrease the scale, steps of 10 uV\n"
+
"Left, right arrow keys: Increase/decrease the time to show, steps of 1 s\n"
+
"Spacebar: Stop the scope plotting, whereas data acquisition keeps running (EXPERIMENTAL)\n"
+ "Esc: Exits the scope",
anchor=(0, 0),
border=(70, 70, 70),
fill=pg.mkColor(20, 20, 20, 200),
color=(255, 255, 255))
# Stop plot functionality
self.stop_plot = 0
# Force repaint even when we shouldn't repaint.
self.force_repaint = 0
# For some strange reason when the width is > 1 px the scope runs slow.
# self.pen_plot = []
# for x in range(0, self.config['eeg_channels']):
# self.pen_plot.append(pg.mkPen(self.colors[x%16,:], width=3))
#
# Initializes the BCI loop parameters
#
def init_loop_cnbiloop(self):
self.fin = open(self.scope_settings.get("internal", "path_pipe"), 'r')
# 12 unsigned ints (4 bytes)
data = struct.unpack("<12I", self.fin.read(4 * 12))
self.config = {
'id': data[0],
'sf': data[1],
'labels': data[2],
'samples': data[3],
'eeg_channels': data[4],
'exg_channels': data[5],
'tri_channels': data[6],
'eeg_type': data[8],
'exg_type': data[9],
'tri_type': data[10],
'lbl_type': data[11],
'tim_size': 1,
'idx_size': 1
}
self.tri = np.zeros(self.config['samples'])
self.eeg = np.zeros(
(self.config['samples'], self.config['eeg_channels']),
dtype=np.float)
self.exg = np.zeros(
(self.config['samples'], self.config['exg_channels']),
dtype=np.float)
# TID initialization
self.bci = BCI.BciInterface()
#
# Initializes the BCI loop parameters
#
def init_loop(self):
self.updating = False
self.sr = StreamReceiver(window_size=1,
buffer_size=10,
amp_serial=self.amp_serial,
amp_name=self.amp_name)
srate = int(self.sr.sample_rate)
# n_channels= self.sr.channels
# 12 unsigned ints (4 bytes)
########## TODO: assumkng 32 samples chunk => make it read from LSL header
data = [
'EEG', srate, ['L', 'R'], 32,
len(self.sr.get_eeg_channels()), 0,
self.sr.get_trigger_channel(), None, None, None, None, None
]
logger.info('Trigger channel is %d' % self.sr.get_trigger_channel())
self.config = {
'id': data[0],
'sf': data[1],
'labels': data[2],
'samples': data[3],
'eeg_channels': data[4],
'exg_channels': data[5],
'tri_channels': data[6],
'eeg_type': data[8],
'exg_type': data[9],
'tri_type': data[10],
'lbl_type': data[11],
'tim_size': 1,
'idx_size': 1
}
self.tri = np.zeros(self.config['samples'])
self.last_tri = 0
self.eeg = np.zeros(
(self.config['samples'], self.config['eeg_channels']),
dtype=np.float)
self.exg = np.zeros(
(self.config['samples'], self.config['exg_channels']),
dtype=np.float)
self.ts_list = []
self.ts_list_tri = []
#
# Initializes the QT timer, which will call the update function every 20 ms
#
def init_timer(self):
self.tm = qc.Timer() # leeq
QtCore.QCoreApplication.processEvents()
QtCore.QCoreApplication.flush()
self.timer = QtCore.QTimer(self)
self.timer.timeout.connect(self.update_loop)
self.timer.start(20)
# QtCore.QTimer.singleShot( 20, self.update_loop )
#
# Main update function (connected to the timer)
#
def update_loop(self):
# Sharing variable to stop at the GUI level
if not self.state.value:
logger.info('Viewer stopped')
sys.exit()
try:
# assert self.updating==False, 'thread destroyed?'
# self.updating= True
# self.handle_tobiid_input() # Read TiDs
self.read_eeg() # Read new chunk
if len(self.ts_list) > 0:
self.filter_signal() # Filter acquired data
self.update_ringbuffers() # Update the plotting infor
if (not self.stop_plot):
self.repaint() # Call paint event
except:
logger.exception('Exception. Dropping into a shell.')
pdb.set_trace()
finally:
# self.updating= False
# using singleShot instead
# QtCore.QTimer.singleShot( 20, self.update_loop )
pass
#
# Read EEG
#
def read_eeg(self):
# if self.updating==True: print( '##### ERROR: thread destroyed ? ######' )
# self.updating= True
try:
# data, self.ts_list= self.sr.inlets[0].pull_chunk(max_samples=self.config['sf']) # [frames][channels]
data, self.ts_list = self.sr.acquire(blocking=False)
# TODO: check and change to these two lines
#self.sr.acquire(blocking=False, decim=DECIM)
#data, self.ts_list = self.sr.get_window()
if len(self.ts_list) == 0:
# self.eeg= None
# self.tri= None
return
n = self.config['eeg_channels']
'''
x= np.array( data )
trg_ch= self.config['tri_channels']
if trg_ch is not None:
self.tri= np.reshape( x[:,trg_ch], (-1,1) ) # samples x 1
self.eeg= np.reshape( x[:,self.sr.eeg_channels], (-1,n) ) # samples x channels
'''
trg_ch = self.config['tri_channels']
if trg_ch is not None:
self.tri = np.reshape(data[:, trg_ch], (-1, 1)) # samples x 1
self.eeg = np.reshape(data[:, self.sr.eeg_channels],
(-1, n)) # samples x channels
if DEBUG_TRIGGER:
# show trigger value
try:
trg_value = max(self.tri)
if trg_value > 0:
logger.info('Received trigger %s' % trg_value)
except:
logger.exception('Error! self.tri = %s' % self.tri)
# Read exg. self.config.samples*self.config.exg_ch, type float
# bexg = np.random.rand( 1, self.config['samples'] * self.config['exg_channels'] )
# self.exg = np.reshape(list(bexg), (self.config['samples'],self.config['exg_channels']))
except WindowsError:
# print('**** Access violation in read_eeg():\n%s\n%s'% (sys.exc_info()[0], sys.exc_info()[1]))
pass
except:
logger.exception()
pdb.set_trace()
#
# Read EEG
#
def read_eeg_cnbiloop(self):
# Reading in python is blocking, so it will wait until having the amount of data needed
# Read timestamp. 1 value, type double
timestamp = struct.unpack("<d", self.fin.read(8 * 1))
# Read index. 1 value, type uint64
index = struct.unpack("<Q", self.fin.read(8 * 1))
# Read labels. self.config.labels, type double
labels = struct.unpack("<" + str(self.config['labels']) + "I",
self.fin.read(4 * self.config['labels']))
# Read eeg. self.config.samples*self.config.eeg_ch, type float
beeg = struct.unpack(
"<" + str(self.config['samples'] * self.config['eeg_channels']) +
"f",
self.fin.read(4 * self.config['samples'] *
self.config['eeg_channels']))
self.eeg = np.reshape(
list(beeg), (self.config['samples'], self.config['eeg_channels']))
# Read exg. self.config.samples*self.config.exg_ch, type float
bexg = struct.unpack(
"<" + str(self.config['samples'] * self.config['exg_channels']) +
"f",
self.fin.read(4 * self.config['samples'] *
self.config['exg_channels']))
self.exg = np.reshape(
list(bexg), (self.config['samples'], self.config['exg_channels']))
# Read tri. self.config.samples*self.config.tri_ch, type float
self.tri = struct.unpack(
"<" + str(self.config['samples'] * self.config['tri_channels']) +
"i",
self.fin.read(4 * self.config['samples'] *
self.config['tri_channels']))
#
# Bandpas + CAR filtering
#
def filter_signal(self):
if (self.apply_bandpass):
for x in range(0, self.eeg.shape[1]):
self.eeg[:, x], self.zi[:,
x] = lfilter(self.b, self.a,
self.eeg[:, x], -1,
self.zi[:, x])
# We only apply CAR if selected AND there are at least 2 channels. Otherwise it makes no sense
if (self.apply_car) and (len(self.channels_to_show_idx) > 1):
self.eeg = np.dot(self.matrix_car, np.transpose(self.eeg))
self.eeg = np.transpose(self.eeg)
#
# Update ringbuffers and events for plotting
#
def update_ringbuffers(self):
# leeq
self.data_plot = np.roll(self.data_plot, -len(self.ts_list), 0)
self.data_plot[-len(self.ts_list):, :] = self.eeg
# We have to remove those indexes that reached time = 0
delete_indices_e = []
delete_indices_c = []
for x in range(0, len(self.events_detected), 2):
xh = int(x / 2)
self.events_detected[x] -= len(self.ts_list) # leeq
if (self.events_detected[x] < 0) and (not self.stop_plot):
delete_indices_e.append(x)
delete_indices_e.append(x + 1)
delete_indices_c.append(xh)
self.events_curves[xh].clear()
self.main_plot_handler.removeItem(self.events_text[xh])
self.events_detected = [
i for j, i in enumerate(self.events_detected)
if j not in delete_indices_e
]
self.events_curves = [
i for j, i in enumerate(self.events_curves)
if j not in delete_indices_c
]
self.events_text = [
i for j, i in enumerate(self.events_text)
if j not in delete_indices_c
]
# Find LPT events and add them
if (self.show_LPT_events) and (not self.stop_plot):
for x in range(len(self.tri)):
tri = int(self.tri[x])
if tri != 0 and (tri > self.last_tri):
self.addEventPlot("LPT", tri)
logger.info('Trigger %d received' % tri)
self.last_tri = tri
#
# Called by repaint()
#
def paintEvent(self, e):
# Distinguish between paint events from timer and event QT widget resizing, clicking etc (sender is None)
# We should only paint when the timer triggered the event.
# Just in case, there's a flag to force a repaint even when we shouldn't repaint
sender = self.sender()
if 'force_repaint' not in self.__dict__.keys():
logger.warning('force_repaint is not set! Is it a Qt bug?')
self.force_repaint = 0
if (sender is None) and (not self.force_repaint):
pass
else:
self.force_repaint = 0
qp = QPainter()
qp.begin(self)
# Update the interface
self.paintInterface(qp)
qp.end()
#
# Update stuff on the interface. Only graphical updates should be added here
#
def paintInterface(self, qp):
# Update EEG channels
for x in range(0, len(self.channels_to_show_idx)):
self.curve_eeg[x].setData(
x=self.x_ticks,
y=self.data_plot[:, self.channels_to_show_idx[x]] -
x * self.scale)
# Update events
for x in range(0, len(self.events_detected), 2):
xh = int(x / 2)
self.events_curves[xh].setData(
x=np.array([
self.x_ticks[self.events_detected[x]],
self.x_ticks[self.events_detected[x]]
]),
y=np.array([
+1.5 * self.scale, -0.5 * self.scale -
self.scale * self.config['eeg_channels']
]))
self.events_text[xh].setPos(self.x_ticks[self.events_detected[x]],
self.scale)
#
# Do necessary stuff when scale has changed
#
def update_plot_scale(self, new_scale):
if (new_scale < 1):
new_scale = 1
# commented out by dbdq.
# else:
# new_scale = new_scale - new_scale%10
self.scale = new_scale
# Y Tick labels
values = []
for x in range(0, len(self.channels_to_show_idx)):
values.append((-x * self.scale,
self.channel_labels[self.channels_to_show_idx[x]]))
values_axis = []
values_axis.append(values)
values_axis.append([])
self.main_plot_handler.getAxis('left').setTicks(values_axis)
self.main_plot_handler.setRange(
yRange=[+self.scale, -self.scale * len(self.channels_to_show_idx)])
self.main_plot_handler.setLabel(axis='left',
text='Scale (uV): ' + str(self.scale))
self.trigger_help()
# We force an immediate repaint to avoid "shakiness".
if (not self.stop_plot):
self.force_repaint = 1
self.repaint()
#
# Do necessary stuff when seconds to show have changed
#
def update_plot_seconds(self, new_seconds):
# Do nothing unless...
if (new_seconds != self.seconds_to_show) and (new_seconds > 0) and (
new_seconds < 100):
self.ui.spinBox_time.setValue(new_seconds)
self.main_plot_handler.setRange(xRange=[0, new_seconds])
self.x_ticks = np.zeros(self.config['sf'] * new_seconds)
for x in range(0, self.config['sf'] * new_seconds):
self.x_ticks[x] = (x * 1) / float(self.config['sf'])
if (new_seconds > self.seconds_to_show):
padded_signal = np.zeros((self.config['sf'] * new_seconds,
self.config['eeg_channels']))
padded_signal[padded_signal.shape[0] -
self.data_plot.shape[0]:, :] = self.data_plot
for x in range(0, len(self.events_detected), 2):
self.events_detected[x] += padded_signal.shape[0] - \
self.data_plot.shape[0]
self.data_plot = padded_signal
else:
for x in range(0, len(self.events_detected), 2):
self.events_detected[x] -= self.data_plot.shape[0] - \
self.config['sf'] * new_seconds
self.data_plot = self.data_plot[self.data_plot.shape[0] -
self.config['sf'] *
new_seconds:, :]
self.seconds_to_show = new_seconds
self.trigger_help()
# We force an immediate repaint to avoid "shakiness".
if (not self.stop_plot):
self.force_repaint = 1
self.repaint()
#
# Handle TOBI iD events
#
def handle_tobiid_input(self):
data = None
try:
data = self.bci.iDsock_bus.recv(512)
self.bci.idStreamer_bus.Append(data)
except:
self.nS = False
self.dec = 0
pass
# deserialize ID message
if data:
if self.bci.idStreamer_bus.Has("<tobiid", "/>"):
msg = self.bci.idStreamer_bus.Extract("<tobiid", "/>")
self.bci.id_serializer_bus.Deserialize(msg)
self.bci.idStreamer_bus.Clear()
tmpmsg = int(self.bci.id_msg_bus.GetEvent())
if (self.show_TID_events) and (not self.stop_plot):
self.addEventPlot("TID", tmpmsg)
elif self.bci.idStreamer_bus.Has("<tcstatus", "/>"):
MsgNum = self.bci.idStreamer_bus.Count("<tcstatus")
for i in range(1, MsgNum - 1):
# Extract most of these messages and trash them
msg_useless = self.bci.idStreamer_bus.Extract(
"<tcstatus", "/>")
#
# Add an event to the scope
#
def addEventPlot(self, event_name, event_id):
if (event_name == "TID"):
color = pg.mkColor(0, 0, 255)
elif (event_name == "KEY"):
color = pg.mkColor(255, 0, 0)
elif (event_name == "LPT"):
color = pg.mkColor(0, 255, 0)
else:
color = pg.mkColor(255, 255, 255)
self.events_detected.append(self.data_plot.shape[0] - 1)
self.events_detected.append(event_id)
self.events_curves.append(
self.main_plot_handler.plot(
pen=color,
x=np.array([self.x_ticks[-1], self.x_ticks[-1]]),
y=np.array([
+1.5 * self.scale,
-1.5 * self.scale * self.config['eeg_channels']
])))
# text = pg.TextItem(event_name + "(" + str(self.events_detected[-1]) + ")", anchor=(1.1,0), fill=(0,0,0), color=color)
text = pg.TextItem(str(self.events_detected[-1]),
anchor=(1.1, 0),
fill=(0, 0, 0),
color=color)
text.setPos(self.x_ticks[-1], self.scale)
self.events_text.append(text)
self.main_plot_handler.addItem(self.events_text[-1])
#
# Calculation of bandpass coefficients.
# Order is computed automatically.
# Note that if filter is unstable this function crashes (TODO handle problems)
#
def butter_bandpass(self, highcut, lowcut, fs, num_ch):
low = lowcut / (0.5 * fs)
high = highcut / (0.5 * fs)
# get the order. TO BE DONE: Sometimes it fails
#ord = buttord(high, low, 2, 40)
#b, a = butter(ord[0], [high, low], btype='band')
b, a = butter(2, [high, low], btype='band')
zi = np.zeros([a.shape[0] - 1, num_ch])
return b, a, zi
#
# Updates the title shown in the scope
#
def update_title_scope(self):
if (hasattr(self, 'main_plot_handler')):
self.main_plot_handler.setTitle(
title='TLK: ' + self.bool_parser[self.show_TID_events] +
self.bool_parser[self.show_LPT_events] +
self.bool_parser[self.show_Key_events] + ', CAR: ' +
self.bool_parser[self.apply_car] + ', BP: ' +
self.bool_parser[self.apply_bandpass] + ' [' +
str(self.ui.doubleSpinBox_hp.value()) + '-' +
str(self.ui.doubleSpinBox_lp.value()) + '] Hz')
# ', BP: ' + self.bool_parser[self.apply_bandpass] + (' [' + str(self.doubleSpinBox_hp.value()) + '-' + str(self.doubleSpinBox_lp.value()) + '] Hz' if self.apply_bandpass else ''))
#
# Shows / hide help in the scope window
#
def trigger_help(self):
if self.show_help:
self.help.setPos(0, self.scale)
self.main_plot_handler.addItem(self.help)
self.help.setZValue(1)
else:
self.main_plot_handler.removeItem(self.help)
# ----------------------------------------------------------------------------------------------------
# EVENT HANDLERS
# ----------------------------------------------------------------------------------------------------
def onClicked_button_rec(self):
# Simply call cl_rpc for this.
if (len(self.lineEdit_recFilename.text()) > 0):
if ".gdf" in self.lineEdit_recFilename.text():
self.ui.pushButton_stoprec.setEnabled(True)
self.ui.pushButton_rec.setEnabled(False)
# Popen is more efficient than os.open, since it is non-blocking
subprocess.Popen([
"cl_rpc", "openxdf",
str(self.ui.lineEdit_recFilename.text()), "dummy_log",
"dummy_log"
],
close_fds=True)
self.statusBar.showMessage(
"Recording file " +
str(self.ui.lineEdit_recFilename.text()))
elif ".bdf" in self.ui.lineEdit_recFilename.text():
self.ui.pushButton_stoprec.setEnabled(True)
self.ui.pushButton_rec.setEnabled(False)
subprocess.Popen([
"cl_rpc", "openxdf",
str(self.ui.lineEdit_recFilename.text()), "dummy_log",
"dummy_log"
],
close_fds=True)
self.statusBar.showMessage(
"Recording file " +
str(self.ui.lineEdit_recFilename.text()))
else:
pass
def onClicked_button_stoprec(self):
subprocess.Popen(["cl_rpc", "closexdf"], close_fds=True)
self.ui.pushButton_rec.setEnabled(True)
self.ui.pushButton_stoprec.setEnabled(False)
self.ui.statusBar.showMessage("Not recording")
def onActivated_checkbox_bandpass(self):
self.apply_bandpass = False
self.ui.pushButton_bp.setEnabled(self.ui.checkBox_bandpass.isChecked())
self.ui.doubleSpinBox_hp.setEnabled(
self.ui.checkBox_bandpass.isChecked())
self.ui.doubleSpinBox_lp.setEnabled(
self.ui.checkBox_bandpass.isChecked())
self.update_title_scope()
def onActivated_checkbox_car(self):
self.apply_car = self.ui.checkBox_car.isChecked()
self.update_title_scope()
def onActivated_checkbox_TID(self):
self.show_TID_events = self.ui.checkBox_showTID.isChecked()
self.update_title_scope()
def onActivated_checkbox_LPT(self):
self.show_LPT_events = self.ui.checkBox_showLPT.isChecked()
self.update_title_scope()
def onActivated_checkbox_Key(self):
self.show_Key_events = self.ui.checkBox_showKey.isChecked()
self.update_title_scope()
def onValueChanged_spinbox_time(self):
self.update_plot_seconds(self.ui.spinBox_time.value())
def onActivated_combobox_scale(self):
self.update_plot_scale(
self.scales_range[self.ui.comboBox_scale.currentIndex()])
def onClicked_button_bp(self):
if (self.ui.doubleSpinBox_lp.value() >
self.ui.doubleSpinBox_hp.value()):
self.apply_bandpass = True
self.b, self.a, self.zi = self.butter_bandpass(
self.ui.doubleSpinBox_hp.value(),
self.ui.doubleSpinBox_lp.value(), self.config['sf'],
self.config['eeg_channels'])
self.update_title_scope()
def onSelectionChanged_table(self):
# Remove current plot
for x in range(0, len(self.channels_to_show_idx)):
self.main_plot_handler.removeItem(self.curve_eeg[x])
# Which channels should I plot?
self.channels_to_show_idx = []
self.channels_to_hide_idx = []
idx = 0
for y in range(0, 4):
for x in range(0, NUM_X_CHANNELS):
if (idx < self.config['eeg_channels']):
#if (self.table_channels.isItemSelected( # Qt4 only
if (QTableWidgetItem.isSelected( # Qt5
self.ui.table_channels.item(x, y))):
self.channels_to_show_idx.append(idx)
else:
self.channels_to_hide_idx.append(idx)
idx += 1
# Add new plots
self.curve_eeg = []
for x in range(0, len(self.channels_to_show_idx)):
self.curve_eeg.append(
self.main_plot_handler.plot(
x=self.x_ticks,
y=self.data_plot[:, self.channels_to_show_idx[x]],
pen=self.colors[self.channels_to_show_idx[x] %
NUM_X_CHANNELS, :]))
self.curve_eeg[-1].setDownsampling(ds=self.subsampling_value,
auto=False,
method="mean")
# Update CAR so it's computed based only on the shown channels
if (len(self.channels_to_show_idx) > 1):
self.matrix_car = np.zeros(
(self.config['eeg_channels'], self.config['eeg_channels']),
dtype=float)
self.matrix_car[:, :] = -1 / float(len(self.channels_to_show_idx))
np.fill_diagonal(self.matrix_car,
1 - (1 / float(len(self.channels_to_show_idx))))
for x in range(0, len(self.channels_to_hide_idx)):
self.matrix_car[self.channels_to_hide_idx[x], :] = 0
self.matrix_car[:, self.channels_to_hide_idx[x]] = 0
# Refresh the plot
self.update_plot_scale(self.scale)
def keyPressEvent(self, event):
key = event.key()
if (key == QtCore.Qt.Key_Escape):
self.closeEvent(None)
if (key == QtCore.Qt.Key_H):
self.show_help = not self.show_help
self.trigger_help()
if (key == QtCore.Qt.Key_Up):
# Python's log(x, 10) has a rounding bug. Use log10(x) instead.
new_scale = self.scale + max(1, 10**int(math.log10(self.scale)))
self.update_plot_scale(new_scale)
if (key == QtCore.Qt.Key_Space):
self.stop_plot = not self.stop_plot
if (key == QtCore.Qt.Key_Down):
if self.scale >= 2:
# Python's log(x, 10) has a rounding bug. Use log10(x) instead.
new_scale = self.scale - max(
1, 10**int(math.log10(self.scale - 1)))
self.update_plot_scale(new_scale)
if (key == QtCore.Qt.Key_Left):
self.update_plot_seconds(self.seconds_to_show - 1)
if (key == QtCore.Qt.Key_Right):
self.update_plot_seconds(self.seconds_to_show + 1)
if (key == QtCore.Qt.Key_L):
self.ui.checkBox_showLPT.setChecked(
not self.ui.checkBox_showLPT.isChecked())
if (key == QtCore.Qt.Key_T):
self.ui.checkBox_showTID.setChecked(
not self.ui.checkBox_showTID.isChecked())
if (key == QtCore.Qt.Key_K):
self.ui.checkBox_showKey.setChecked(
not self.ui.checkBox_showKey.isChecked())
if (key == QtCore.Qt.Key_C):
self.ui.checkBox_car.setChecked(
not self.ui.checkBox_car.isChecked())
if (key == QtCore.Qt.Key_B):
self.ui.checkBox_bandpass.setChecked(
not self.ui.checkBox_bandpass.isChecked())
if self.ui.checkBox_bandpass.isChecked():
self.ui.pushButton_bp.click()
if ((key >= QtCore.Qt.Key_0) and (key <= QtCore.Qt.Key_9)):
if (self.show_Key_events) and (not self.stop_plot):
self.addEventPlot("KEY", 990 + key - QtCore.Qt.Key_0)
# self.bci.id_msg_bus.SetEvent(990 + key - QtCore.Qt.Key_0)
# self.bci.iDsock_bus.sendall(self.bci.id_serializer_bus.Serialize());
# 666
#
# Function called when a closing event was triggered.
#
def closeEvent(self, event):
'''
reply = QtGui.QMessageBox.question(self, "Quit", "Are you sure you want to quit?", QtGui.QMessageBox.Yes | QtGui.QMessageBox.No, QtGui.QMessageBox.Yes)
if (reply == QtGui.QMessageBox.Yes):
if (self.pushButton_stoprec.isEnabled()):
subprocess.Popen(["cl_rpc", "closexdf"], close_fds=True)
self.fin.close()
exit()
'''
# leeq
if (self.ui.pushButton_stoprec.isEnabled()):
subprocess.Popen(["cl_rpc", "closexdf"], close_fds=True)
with self.state.get_lock():
self.state.value = 0
watchdog = qc.Timer()
tm = qc.Timer(autoreset=True)
trg_ch = sr.get_trigger_channel()
last_ts = 0
# qc.print_c('Trigger channel: %d' % trg_ch, 'G')
fmin = 1
fmax = 40
psde = mne.decoding.PSDEstimator(
sfreq=sfreq, fmin=fmin, fmax=fmax, bandwidth=None,
adaptive=False, low_bias=True, n_jobs=1,
normalization='length', verbose=None
)
while True:
sr.acquire()
window, tslist = sr.get_window() # window = [samples x channels]
window = window.T # chanel x samples
# print event values
tsnew = np.where(np.array(tslist) > last_ts)[0][0]
trigger = np.unique(window[trg_ch, tsnew:])
if len(trigger) > 0:
qc.print_c('Triggers: %s' % np.array(trigger), 'G')
# print('[%.1f] Receiving data...' % watchdog.sec())
# ADD YOUR CODE
unused_channels = ['TRIGGER', 'X1', 'X2', 'X3', 'A2']
brainhack.eeg_ch_names = EEG_CH_NAMES.copy()
qc.print_c('Trigger channel: %d' % trg_ch, 'G')
if SHOW_PSD:
psde = mne.decoding.PSDEstimator(sfreq=sfreq, fmin=1, fmax=50, bandwidth=None,
adaptive=False, low_bias=True, n_jobs=1, normalization='length', verbose=None)
sfreq = 512
interval = 1. / sfreq
fig, ax = plt.subplots(1, 1)
time_range = 1
x = np.arange(0, time_range, interval)
y = [0]*(time_range*sfreq)
lines, = ax.plot(x, y)
while True:
stream_receiver.acquire()
# window = [samples x channels]
window, tslist = stream_receiver.get_window()
window = window.T # chanel x samples
# print(window)
# # print event values
# tsnew = np.where(np.array(tslist) > last_ts)[0][0]
# trigger = np.unique(window[trg_ch, tsnew:])
# for Biosemi
# if sr.amp_name=='BioSemi':
# trigger= set( [255 & int(x-1) for x in trigger ] )
# if len(trigger) > 0:
# qc.print_c('Triggers: %s' % np.array(trigger), 'G')
class RecordingFromHardWare():
def __init__(self):
self.start_recording = False
self.new_data = []
self.sr = StreamReceiver(buffer_size=0)
self.record_dir = '%s/records' % os.getcwd()
self.current_window = np.ndarray([])
self.current_time_stamps = np.ndarray([])
self.MRCP_window_size = 6
print("I RUMNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN")
# =============================================================================
# def runInfinitecording(self):
# while self.start_recording:
# self.sr.acquire()
# if self.sr.get_buflen() > 20:
# duration = str(datetime.timedelta(seconds=int(self.sr.get_buflen())))
# #recordLogger.info('RECORDING %s' % duration)
# #next_sec += 1
# buffers, times = self.sr.get_buffer()
# signals = buffers
# events = None
#
# # channels = total channels from amp, including trigger channel
# data = {'signals':signals, 'timestamps':times, 'events':events,
# 'sample_rate':self.sr.get_sample_rate(), 'channels':self.sr.get_num_channels(),
# 'ch_names':self.sr.get_channel_names(), 'lsl_time_offset':self.sr.lsl_time_offset}
# print(data)
# self.new_data.append(data)
# =============================================================================
def startRecording(self):
# do some stuff
self.start_recording = True
#download_thread = threading.Thread(target=self.runInfinitecording)
recordLogger = logger
thread = threading.Thread(target=self.record)
thread.start()
def stopRecording(self):
self.start_recording = False
#Write into file
#self.new_data = []
def isRecordingIsRunning(self):
return self.start_recording
def set_MRCP_window_size(self, MRCP_window_size):
self.MRCP_window_size = MRCP_window_size
def record(self):
recordLogger = logger
#redirect_stdout_to_queue(recordLogger, 'INFO')
# set data file name
timestamp = time.strftime('%Y%m%d-%H%M%S', time.localtime())
pcl_file = "%s/%s-raw.pcl" % (self.record_dir, timestamp)
eve_file = '%s/%s-eve.txt' % (self.record_dir, timestamp)
recordLogger.info('>> Output file: %s' % (pcl_file))
# test writability
try:
qc.make_dirs(self.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, eeg_only=eeg_only)
# start recording
recordLogger.info('\n>> Recording started (PID %d).' % os.getpid())
qc.print_c('\n>> Press Enter to stop recording', 'G')
tm = qc.Timer(autoreset=True)
next_sec = 1
while self.start_recording:
self.sr.acquire()
if self.sr.get_buflen() > next_sec:
duration = str(
datetime.timedelta(seconds=int(self.sr.get_buflen())))
#recordLogger.info('RECORDING %s' % duration)
next_sec += 1
#current_buffer, current_times = self.sr.get_buffer()
self.sr.set_window_size(self.MRCP_window_size)
self.current_window, self.current_time_stamps = self.sr.get_window(
)
#print("window shape: {} time stamps shape: {}".format(self.current_window.shape, self.current_time_stamps.shape))
tm.sleep_atleast(0.001)
# record stop
recordLogger.info('>> Stop requested. Copying buffer')
buffers, times = self.sr.get_buffer()
signals = buffers
events = None
# channels = total channels from amp, including trigger channel
data = {
'signals': signals,
'timestamps': times,
'events': events,
'sample_rate': self.sr.get_sample_rate(),
'channels': self.sr.get_num_channels(),
'ch_names': self.sr.get_channel_names(),
'lsl_time_offset': self.sr.lsl_time_offset
}
print("data length : {}".format(data['signals'].shape))
self.new_data = data['signals']
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)
class BCIDecoder(object):
"""
Decoder class
The label order of self.labels and self.label_names match likelihood orders computed by get_prob()
"""
def __init__(self, classifier=None, buffer_size=1.0, fake=False, amp_serial=None, amp_name=None):
"""
Params
------
classifier: classifier file
spatial: spatial filter to use
buffer_size: length of the signal buffer in seconds
"""
self.classifier = classifier
self.buffer_sec = buffer_size
self.fake = fake
self.amp_serial = amp_serial
self.amp_name = amp_name
if self.fake == False:
model = qc.load_obj(self.classifier)
if model == None:
self.print('Error loading %s' % model)
sys.exit(-1)
self.cls = model['cls']
self.psde = model['psde']
self.labels = list(self.cls.classes_)
self.label_names = [model['classes'][k] for k in self.labels]
self.spatial = model['spatial']
self.spectral = model['spectral']
self.notch = model['notch']
self.w_seconds = model['w_seconds']
self.w_frames = model['w_frames']
self.wstep = model['wstep']
self.sfreq = model['sfreq']
if not int(self.sfreq * self.w_seconds) == self.w_frames:
raise RuntimeError('sfreq * w_sec %d != w_frames %d' % (int(self.sfreq * self.w_seconds), self.w_frames))
if 'multiplier' in model:
self.multiplier = model['multiplier']
else:
self.multiplier = 1
# Stream Receiver
self.sr = StreamReceiver(window_size=self.w_seconds, amp_name=self.amp_name, amp_serial=self.amp_serial)
if self.sfreq != self.sr.sample_rate:
raise RuntimeError('Amplifier sampling rate (%.1f) != model sampling rate (%.1f). Stop.' % (
self.sr.sample_rate, self.sfreq))
# Map channel indices based on channel names of the streaming server
self.spatial_ch = model['spatial_ch']
self.spectral_ch = model['spectral_ch']
self.notch_ch = model['notch_ch']
self.ref_new = model['ref_new']
self.ref_old = model['ref_old']
self.ch_names = self.sr.get_channel_names()
mc = model['ch_names']
self.picks = [self.ch_names.index(mc[p]) for p in model['picks']]
if self.spatial_ch is not None:
self.spatial_ch = [self.ch_names.index(mc[p]) for p in model['spatial_ch']]
if self.spectral_ch is not None:
self.spectral_ch = [self.ch_names.index(mc[p]) for p in model['spectral_ch']]
if self.notch_ch is not None:
self.notch_ch = [self.ch_names.index(mc[p]) for p in model['notch_ch']]
if self.ref_new is not None:
self.ref_new = self.ch_names.index(mc[model['ref_new']])
if self.ref_old is not None:
self.ref_old = self.ch_names.index(mc[model['ref_old']])
# PSD buffer
psd_temp = self.psde.transform(np.zeros((1, len(self.picks), self.w_frames)))
self.psd_shape = psd_temp.shape
self.psd_size = psd_temp.size
self.psd_buffer = np.zeros((0, self.psd_shape[1], self.psd_shape[2]))
self.ts_buffer = []
else:
# Fake left-right decoder
model = None
self.psd_shape = None
self.psd_size = None
# TODO: parameterize directions using fake_dirs
self.labels = [11, 9]
self.label_names = ['LEFT_GO', 'RIGHT_GO']
def print(self, *args):
if len(args) > 0: print('[BCIDecoder] ', end='')
print(*args)
def get_labels(self):
"""
Returns
-------
Class labels numbers in the same order as the likelihoods returned by get_prob()
"""
return self.labels
def get_label_names(self):
"""
Returns
-------
Class label names in the same order as get_labels()
"""
return self.label_names
def start(self):
pass
def stop(self):
pass
def get_prob(self):
"""
Read the latest window
Returns
-------
The likelihood P(X|C), where X=window, C=model
"""
if self.fake:
# fake deocder: biased likelihood for the first class
probs = [random.uniform(0.0, 1.0)]
# others class likelihoods are just set to equal
p_others = (1 - probs[0]) / (len(self.labels) - 1)
for x in range(1, len(self.labels)):
probs.append(p_others)
time.sleep(0.0625) # simulated delay for PSD + RF
else:
self.sr.acquire()
w, ts = self.sr.get_window() # w = times x channels
w = w.T # -> channels x times
# apply filters. Important: maintain the original channel order at this point.
pu.preprocess(w, sfreq=self.sfreq, spatial=self.spatial, spatial_ch=self.spatial_ch,
spectral=self.spectral, spectral_ch=self.spectral_ch, notch=self.notch,
notch_ch=self.notch_ch, multiplier=self.multiplier)
# select the same channels used for training
w = w[self.picks]
# debug: show max - min
# c=1; print( '### %d: %.1f - %.1f = %.1f'% ( self.picks[c], max(w[c]), min(w[c]), max(w[c])-min(w[c]) ) )
# psd = channels x freqs
psd = self.psde.transform(w.reshape((1, w.shape[0], w.shape[1])))
# update psd buffer ( < 1 msec overhead )
self.psd_buffer = np.concatenate((self.psd_buffer, psd), axis=0)
self.ts_buffer.append(ts[0])
if ts[0] - self.ts_buffer[0] > self.buffer_sec:
# search speed comparison for ordered arrays:
# http://stackoverflow.com/questions/16243955/numpy-first-occurence-of-value-greater-than-existing-value
t_index = np.searchsorted(self.ts_buffer, ts[0] - 1.0)
self.ts_buffer = self.ts_buffer[t_index:]
self.psd_buffer = self.psd_buffer[t_index:, :, :] # numpy delete is slower
# assert ts[0] - self.ts_buffer[0] <= self.buffer_sec
# make a feautre vector and classify
feats = np.concatenate(psd[0]).reshape(1, -1)
# compute likelihoods
probs = self.cls.predict_proba(feats)[0]
return probs
def get_prob_unread(self):
return self.get_prob()
def get_psd(self):
"""
Returns
-------
The latest computed PSD
"""
return self.psd_buffer[-1].reshape((1, -1))
def is_ready(self):
"""
Ready to decode? Returns True if buffer is not empty.
"""
return self.sr.is_ready()
# Frequency band for the band-pass filter
fmin = 1
fmax = 47
# Create an estimator of the power spectrum density in the frequency band
psde = mne.decoding.PSDEstimator(sfreq=sfreq,
fmin=fmin,
fmax=fmax,
bandwidth=None,
adaptive=False,
low_bias=True,
n_jobs=1,
normalization='length',
verbose=None)
# Main loop
while True:
sr.acquire() # Read data from LSL
window, tslist = sr.get_window() # window = [samples x channels]
window = window.T # channels x samples
# print event values
tsnew = np.where(np.array(tslist) > last_ts)[0][0]
trigger = np.unique(window[trg_ch, tsnew:])
# if len(trigger) > 0:
# qc.print_c('Triggers: %s' % np.array(trigger), 'G')
# print('[%.1f] Receiving data...' % watchdog.sec())
# Pass data to the processing code
# - bandpass it
# - apply the inverse solution to get a signal for all cortical sources
class BCIDecoder(object):
"""
Decoder class
The label order of self.labels and self.label_names match likelihood orders computed by get_prob()
"""
def __init__(self, classifier=None, buffer_size=1.0, fake=False, amp_serial=None, amp_name=None):
"""
Params
------
classifier: classifier file
spatial: spatial filter to use
buffer_size: length of the signal buffer in seconds
"""
self.classifier = classifier
self.buffer_sec = buffer_size
self.fake = fake
self.amp_serial = amp_serial
self.amp_name = amp_name
if self.fake == False:
model = qc.load_obj(self.classifier)
if model is None:
logger.error('Classifier model is None.')
raise ValueError
self.cls = model['cls']
self.psde = model['psde']
self.labels = list(self.cls.classes_)
self.label_names = [model['classes'][k] for k in self.labels]
self.spatial = model['spatial']
self.spectral = model['spectral']
self.notch = model['notch']
self.w_seconds = model['w_seconds']
self.w_frames = model['w_frames']
self.wstep = model['wstep']
self.sfreq = model['sfreq']
if 'decim' not in model:
model['decim'] = 1
self.decim = model['decim']
if not int(round(self.sfreq * self.w_seconds)) == self.w_frames:
logger.error('sfreq * w_sec %d != w_frames %d' % (int(round(self.sfreq * self.w_seconds)), self.w_frames))
raise RuntimeError
if 'multiplier' in model:
self.multiplier = model['multiplier']
else:
self.multiplier = 1
# Stream Receiver
self.sr = StreamReceiver(window_size=self.w_seconds, amp_name=self.amp_name, amp_serial=self.amp_serial)
if self.sfreq != self.sr.sample_rate:
logger.error('Amplifier sampling rate (%.3f) != model sampling rate (%.3f). Stop.' % (self.sr.sample_rate, self.sfreq))
raise RuntimeError
# Map channel indices based on channel names of the streaming server
self.spatial_ch = model['spatial_ch']
self.spectral_ch = model['spectral_ch']
self.notch_ch = model['notch_ch']
#self.ref_ch = model['ref_ch'] # not supported yet
self.ch_names = self.sr.get_channel_names()
mc = model['ch_names']
self.picks = [self.ch_names.index(mc[p]) for p in model['picks']]
if self.spatial_ch is not None:
self.spatial_ch = [self.ch_names.index(mc[p]) for p in model['spatial_ch']]
if self.spectral_ch is not None:
self.spectral_ch = [self.ch_names.index(mc[p]) for p in model['spectral_ch']]
if self.notch_ch is not None:
self.notch_ch = [self.ch_names.index(mc[p]) for p in model['notch_ch']]
# PSD buffer
#psd_temp = self.psde.transform(np.zeros((1, len(self.picks), self.w_frames // self.decim)))
#self.psd_shape = psd_temp.shape
#self.psd_size = psd_temp.size
#self.psd_buffer = np.zeros((0, self.psd_shape[1], self.psd_shape[2]))
#self.psd_buffer = None
self.ts_buffer = []
logger.info_green('Loaded classifier %s (sfreq=%.3f, decim=%d)' % (' vs '.join(self.label_names), self.sfreq, self.decim))
else:
# Fake left-right decoder
model = None
self.psd_shape = None
self.psd_size = None
# TODO: parameterize directions using fake_dirs
self.labels = [11, 9]
self.label_names = ['LEFT_GO', 'RIGHT_GO']
def get_labels(self):
"""
Returns
-------
Class labels numbers in the same order as the likelihoods returned by get_prob()
"""
return self.labels
def get_label_names(self):
"""
Returns
-------
Class label names in the same order as get_labels()
"""
return self.label_names
def start(self):
pass
def stop(self):
pass
def get_prob(self, timestamp=False):
"""
Read the latest window
Input
-----
timestamp: If True, returns LSL timestamp of the leading edge of the window used for decoding.
Returns
-------
The likelihood P(X|C), where X=window, C=model
"""
if self.fake:
# fake deocder: biased likelihood for the first class
probs = [random.uniform(0.0, 1.0)]
# others class likelihoods are just set to equal
p_others = (1 - probs[0]) / (len(self.labels) - 1)
for x in range(1, len(self.labels)):
probs.append(p_others)
time.sleep(0.0625) # simulated delay
t_prob = pylsl.local_clock()
else:
self.sr.acquire(blocking=True)
w, ts = self.sr.get_window() # w = times x channels
t_prob = ts[-1]
w = w.T # -> channels x times
# re-reference channels
# TODO: add re-referencing function to preprocess()
# apply filters. Important: maintain the original channel order at this point.
w = pu.preprocess(w, sfreq=self.sfreq, spatial=self.spatial, spatial_ch=self.spatial_ch,
spectral=self.spectral, spectral_ch=self.spectral_ch, notch=self.notch,
notch_ch=self.notch_ch, multiplier=self.multiplier, decim=self.decim)
# select the same channels used for training
w = w[self.picks]
# debug: show max - min
# c=1; print( '### %d: %.1f - %.1f = %.1f'% ( self.picks[c], max(w[c]), min(w[c]), max(w[c])-min(w[c]) ) )
# psd = channels x freqs
psd = self.psde.transform(w.reshape((1, w.shape[0], w.shape[1])))
# make a feautre vector and classify
feats = np.concatenate(psd[0]).reshape(1, -1)
# compute likelihoods
probs = self.cls.predict_proba(feats)[0]
# update psd buffer ( < 1 msec overhead )
'''
if self.psd_buffer is None:
self.psd_buffer = psd
else:
self.psd_buffer = np.concatenate((self.psd_buffer, psd), axis=0)
# TODO: CHECK THIS BLOCK
self.ts_buffer.append(ts[0])
if ts[0] - self.ts_buffer[0] > self.buffer_sec:
# search speed comparison for ordered arrays:
# http://stackoverflow.com/questions/16243955/numpy-first-occurence-of-value-greater-than-existing-value
#t_index = np.searchsorted(self.ts_buffer, ts[0] - 1.0)
t_index = np.searchsorted(self.ts_buffer, ts[0] - self.buffer_sec)
self.ts_buffer = self.ts_buffer[t_index:]
self.psd_buffer = self.psd_buffer[t_index:, :, :] # numpy delete is slower
# assert ts[0] - self.ts_buffer[0] <= self.buffer_sec
'''
if timestamp:
return probs, t_prob
else:
return probs
def get_prob_unread(self, timestamp=False):
return self.get_prob(timestamp)
def get_psd(self):
"""
Returns
-------
The latest computed PSD
"""
raise NotImplementedError('Sorry! PSD buffer is under testing.')
return self.psd_buffer[-1].reshape((1, -1))
def is_ready(self):
"""
Ready to decode? Returns True if buffer is not empty.
"""
return self.sr.is_ready()