for stream in streams:
print('results >>', stream)
print("stream_name", stream.name())
print("/--------------/")
# first resolve an EEG stream on the lab network
print("looking for an EEG stream...")
name1 = 'obci_eeg'
name2 = 'obci_aux'
mac1 = 'EEG'
mac2 = 'AUX'
stream1 = resolve_stream('name', name1)
#stream2 = resolve_stream('type', mac2)
# create a new inlet to read from the stream
inlet1 = StreamInlet(stream1[0])
#inlet2 = StreamInlet(stream2[0])
stream_info = inlet1.info()
stream_name = stream_info.name()
stream_mac = stream_info.type()
stream_host = stream_info.hostname()
stream_n_channels = stream_info.channel_count()
print(stream_info)
print(stream_name)
print(stream_mac)
print(stream_host)
print(stream_n_channels)
#wait before streaming
time.sleep(10)
while True:
help="Name of the recording file.")
# dejitter timestamps
dejitter = False
(options, args) = parser.parse_args()
print("looking for an EEG stream...")
streams = resolve_byprop('type', 'EEG', timeout=2)
if len(streams) == 0:
raise(RuntimeError, "Can't find EEG stream")
print("Start acquiring data")
inlet = StreamInlet(streams[0], max_chunklen=12)
eeg_time_correction = inlet.time_correction()
print("looking for a Markers stream...")
marker_streams = resolve_byprop('name', 'Markers', timeout=2)
if marker_streams:
inlet_marker = StreamInlet(marker_streams[0])
marker_time_correction = inlet_marker.time_correction()
else:
inlet_marker = False
print("Can't find Markers stream")
info = inlet.info()
description = info.desc()
Also, the # Chan should match the data type (Examples: 1 for Focus, 3 for Accel)
"""
from pylsl import StreamInlet, resolve_stream
import time
numStreams = 3
# first resolve an EEG stream on the lab network
print("looking for an EEG stream...")
stream1 = resolve_stream('type', 'EEG')
stream2 = resolve_stream('type', 'AUX')
stream3 = resolve_stream('type', 'FFT')
# create a new inlet to read from the stream
inlet = StreamInlet(stream1[0])
inlet2 = StreamInlet(stream2[0])
inlet3 = StreamInlet(stream3[0])
def testLSLSamplingRates():
print( "Testing Sampling Rates..." )
start = time.time()
numSamples1 = 0
numSamples2 = 0
numSamples3 = 0
while time.time() < start + 5:
# get a new sample (you can also omit the timestamp part if you're not
# interested in it)
for i in range(numStreams):
if i == 0:
chunk, timestamps = inlet.pull_chunk()
# Connect to read EEG
stream_name = 'LSLoutlet-EEG'
streams = resolve_stream('type', 'EEG')
try:
for i in range (len(streams)):
print(streams[i].name())
if (streams[i].name() == stream_name):
index = i
print ("NIC stream available")
print ("Connecting to NIC stream... \n")
inlet = StreamInlet(streams[index])
except NameError:
print ("Error: NIC stream not available\n\n\n")
else:
raise ValueError('Invalid EEG device number')
# Ensure EEG is connected
[clock, initial_timestamp] = connectEEG(EEGdevice, outlet)
# Load our classifier
# clf, X_loaded = loadModel(subjID)
# Save task parameters
taskParameters = {'initial_timestamp':initial_timestamp, \
'column_count':COLUMN_COUNT,'row_count':ROW_COUNT,'width':WIDTH,\
def present(duration=120):
print('Looking for an EEG stream...')
streams = resolve_byprop('type', 'EEG', timeout=2)
if len(streams) == 0:
raise RuntimeError('Can\'t find EEG stream.')
#EEG Data Aquisition
print("Start acquiring data")
inlet = StreamInlet(streams[0], max_chunklen=12)
#outlet = StreamOutlet(info)
eeg_time_correction = inlet.time_correction()
markernames = [1, 2]
start = time()
# Set up trial parameters
n_trials = 2010
iti = 0.3
soa = 0.2
jitter = 0.2
record_duration = np.float32(120)
duration = 120
subject = 1
session = 1
recording_path = os.path.join(os.path.expanduser("~"), "eeg-notebooks", "data", "visual", "P300", "subject" + str(subject), "session" + str(session), ("recording_%s.csv" %
strftime("%Y-%m-%d-%H.%M.%S", gmtime())) + ".csv")
stimulus = Process(target=.present, args=(duration,))
recording = Process(target=record, args=(duration, recording_path))
stimulus.start()
recording.start()
# Initialize stimuli
aud1 = sound.Sound('C', octave=5, sampleRate=44100, secs=0.2)
aud1.setVolume(0.8)
aud2 = sound.Sound('D', octave=6, sampleRate=44100, secs=0.2)
aud2.setVolume(0.8)
auds = [aud1, aud2]
# Setup trial list
sound_ind = np.random.binomial(1, 0.25, n_trials)
trials = DataFrame(dict(sound_ind=sound_ind, timestamp=np.zeros(n_trials)))
# Setup graphics
mon = monitors.Monitor('test')#fetch the most recent calib for this monitor
mon.setDistance(114)#further away than normal?
mywin = visual.Window(size=[1024,768], monitor=mon)
fixation = visual.GratingStim(win=mywin, size=0.2, pos=[0, 0], sf=0,
rgb=[1, 0, 0])
fixation.setAutoDraw(True)
mywin.flip()
for ii, trial in trials.iterrows():
# Intertrial interval
core.wait(iti + np.random.rand() * jitter)
# Select and play sound
ind = trials['sound_ind'].iloc[ii]
auds[ind].play()
# Send marker
timestamp = time()
#outlet.push_sample([markernames[ind]], timestamp)
# offset
core.wait(soa)
if len(event.getKeys()) > 0 or (time() - start) > record_duration:
break
event.clearEvents()
# Cleanup
mywin.close()
import numpy as np
from pylsl import StreamInlet, resolve_stream, local_clock
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtGui
plot_duration = 2.0
# first resolve an EEG stream on the lab network
print("looking for an EEG stream...")
streams = resolve_stream('type', 'EEG')
# create a new inlet to read from the stream
inlet = StreamInlet(streams[0])
markers = StreamInlet(resolve_stream('type', 'Markers')[0])
# Create the pyqtgraph window
win = pg.GraphicsWindow()
win.setWindowTitle('LSL Plot ' + inlet.info().name())
plt = win.addPlot()
plt.setLimits(xMin=0.0,
xMax=plot_duration,
yMin=-1.0 * (inlet.channel_count - 1),
yMax=1.0)
t0 = [local_clock()] * inlet.channel_count
curves = []
for ch_ix in range(inlet.channel_count):
curves += [plt.plot()]
def update():
def main():
print("Waiting for inference sample data stream " +
config.INFERENCE_LSL_NAME + '...')
streams = resolve_byprop('type', config.INFERENCE_LSL_NAME)
# create a new inlet to read from the stream ######################################################################
inlet = StreamInlet(streams[0])
print('number of streams is ' + str(len(streams)))
# create a outlet to relay the inference results ##################################################################
print('Sample stream found, creating inference results out stream ...')
lsl_data_type = config.INFERENCE_LSL_RESULTS_TYPE
lsl_data_name = config.INFERENCE_LSL_RESULTS_NAME
info = StreamInfo(lsl_data_name,
lsl_data_type,
channel_count=config.INFERENCE_CLASS_NUM,
channel_format='float32',
source_id='myuid1234')
info.desc().append_child_value("apocalyvec", "RealityNavigation")
chns = info.desc().append_child("inference")
channel_names = [
'class' + str(i) for i in range(config.INFERENCE_CLASS_NUM)
]
for label in channel_names:
ch = chns.append_child("channel")
ch.append_child_value("label", label)
outlet = StreamOutlet(info, max_buffered=360)
start_time = local_clock()
# Load inference parameters ########################################################################################
model, y_encoder, data_downsampled_min, data_downsampled_max = load_model_params(
)
# data buffers #####################################################################
timestamp_accumulated = []
print('Entering inference loop')
while True:
# get a new sample (you can also omit the timestamp part if you're not
# interested in it)
try:
data, timestamp = inlet.pull_sample(timeout=1e-2)
if data:
timestamp_accumulated.append(
timestamp) # accumulate timestamps
samples = np.reshape(
data,
newshape=(config.EYE_SAMPLES_PER_INFERENCE,
config.EYE_INFERENCE_WINDOW_TIMESTEPS,
2)) # take the most recent samples
# conduct inference
samples_preprocessed = preprocess_eye_samples(
samples, data_downsampled_max, data_downsampled_min)
results, results_decoded = inference(samples_preprocessed,
model, y_encoder)
# send the inference results via LSL, only send out the not-decoded results
results_moving_average = np.mean(results, axis=0)
print(results_moving_average)
outlet.push_sample(results_moving_average)
inference_per_second = len(timestamp_accumulated) / (
timestamp_accumulated[-1] -
timestamp_accumulated[0]) if timestamp_accumulated[
-1] - timestamp_accumulated[0] != 0. else 0.
print('Inference per second is ' + str(inference_per_second))
print()
except KeyboardInterrupt:
pass
def connect(self, prop='type', value='EEG'):
streams = resolve_byprop(prop, value, timeout=5)
self.inlet = StreamInlet(streams[0], max_chunklen=12)
return self.inlet is not None
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 9 14:01:59 2020
@author: AlexVosk
"""
from pylsl import StreamInlet, resolve_stream
import time
#streams = resolve_stream()
streams = resolve_stream('name', 'EBNeuro_BePLusLTM_192.168.171.81')
print(streams)
time.sleep(1)
stream = streams[0]
ecogInlet = StreamInlet(stream, 2048)
print(stream.name())
time.sleep(1)
a = ecogInlet.pull_sample()
print(a)
stdsmooth = [
0,
] * len(trims)
# stdsmooth = [plotThresh, ] * len(trims)
"""
Initialise LSL:
"""
if online:
""" INLET """
# First resolve an RR stream on the lab network
print "\nlooking for an RR stream..."
RRStream = resolve_stream('type', 'RR')
print "found an RR stream."
# create a new inlet to read from the stream
# (Don't attempt to recover if data lost)
RRInlet = StreamInlet(RRStream[0], recover=False)
""" OUTLET """
# Now create a new stream info object:
# (here we set the name to Faros, the content-type to ECG, 1 channel, frequency = rate,
# float-valued data, and the MAC address)
# The last value, the MAC address of the device, is a local identifier for the stream
# (you could also omit it but interrupted connections wouldn't auto-recover).
# streamConfig = StreamInfo('Emulator', streamType, 1, rate, 'float32', 'Emulator');
streamID = 'RR_STD_CALC_' + environ['COMPUTERNAME']
channel_count = len(trims)
print "Created outlet with ID:", streamID, 'with', channel_count, 'channel(s)'
streamConfig = StreamInfo('RR_STD_CALC', 'HRV_STD', channel_count, 100,
'float32', streamID)
# next add an outlet to the list
RRoutlet = StreamOutlet(streamConfig)
print "LSL channels opened\n"
def record_to_CSV_experiment_data(EXPERIMENT_TIME=5,
SAVE_FILE_NAME='recorded_signal_data',
SHOW_PLT_DATA=False,
DO_PRINT_SAMPLE=False):
"""
Resolve a pylsl stream coming from the plugin streamer lsl that can be
activate by starting the user.py function (in OPENBCI) with the command
-add (ex: for Windows: python user.py -p=COM3 --add streamer_lsl).
Then append this data to a list that is converted to a numpy array
to then save as a CSV file
Parameters:
EXPERIMENT_TIME : int
The duration of streaming of the electrical signal
SAVE_FILE_NAME : str
The name of the file where you want to save the electrical
signal data
SHOW_PLT_DATA : bool
To indicate if you want to plot the acquired data with matplotlib
DO_PRINT_SAMPLE : bool
Toggle this bool if you want to show all the pulled sample
Returns:
all_eeg : np.array
Array containing all the 8 channel data recorded from the
OPENBCI board. These are the same value saved by the function
in the CSV file
timestamps : np.array
Array containing the timestamps for all the acquiered signal values
"""
print("Looking for an EEG stream...")
# Create a new inlet to read from the outlet stream
streams = resolve_stream('type', 'EEG')
inlet = StreamInlet(streams[0])
# Initialisation of values
all_eeg = []
timestamps = []
time_init = time.time()
# Collect data for the duration of the experiment
while time.time() - time_init < EXPERIMENT_TIME:
sample, timestamp = inlet.pull_sample()
if timestamp:
if DO_PRINT_SAMPLE:
print('chunk: ', sample)
timestamps.append(timestamp)
all_eeg.append(sample)
# Show the data for all the cython chanels
all_eeg = np.array(all_eeg)
timestamps = np.array(timestamps)
timestamps_and_eeg = np.concatenate((all_eeg.T, timestamps[:, None].T))
# Show matplotlib of the acquiered data
if SHOW_PLT_DATA:
for i in range(all_eeg.shape[1]):
plt.figure(i)
# Use int(len(all_eeg) * 0.2) avoid showing the noise
# at the beginning # HINT: this should be remove if real testing
plt.plot(all_eeg[int(len(all_eeg) * 0.2):, i])
plt.plot()
# Save as csv
np.savetxt('{SAVE_FILE_NAME}.csv'.format(SAVE_FILE_NAME=SAVE_FILE_NAME),
timestamps_and_eeg.T,
delimiter=',')
return timestamps, all_eeg
def record(duration, filename=None, dejitter=False, data_source="EEG"):
print('dgafsjkfsdgajkp')
chunk_length = LSL_EEG_CHUNK
if data_source == "PPG":
chunk_length = LSL_PPG_CHUNK
if data_source == "ACC":
chunk_length = LSL_ACC_CHUNK
if data_source == "GYRO":
chunk_length = LSL_GYRO_CHUNK
if not filename:
filename = os.path.join(
os.getcwd(), "%s_recording_%s.csv" %
(data_source, strftime('%Y-%m-%d-%H.%M.%S', gmtime())))
print("Looking for a %s stream..." % (data_source))
streams = resolve_byprop('type', data_source, timeout=LSL_SCAN_TIMEOUT)
if len(streams) == 0:
print("Can't find %s stream." % (data_source))
return
print("Started acquiring data.")
inlet = StreamInlet(streams[0], max_chunklen=chunk_length)
# eeg_time_correction = inlet.time_correction()
print("Looking for a Markers stream...")
marker_streams = resolve_byprop('name',
'Markers',
timeout=LSL_SCAN_TIMEOUT)
if marker_streams:
inlet_marker = StreamInlet(marker_streams[0])
else:
inlet_marker = False
print("Can't find Markers stream.")
info = inlet.info()
description = info.desc()
Nchan = info.channel_count()
ch = description.child('channels').first_child()
ch_names = [ch.child_value('label')]
for i in range(1, Nchan):
ch = ch.next_sibling()
ch_names.append(ch.child_value('label'))
res = []
timestamps = []
markers = []
t_init = time()
time_correction = inlet.time_correction()
print('Start recording at time t=%.3f' % t_init)
print('Time correction: ', time_correction)
print('dgafsjkfsdgajkp')
while (time() - t_init) < duration:
try:
data, timestamp = inlet.pull_chunk(timeout=1.0,
max_samples=chunk_length)
print('data = ', data, timestamp)
if timestamp:
res.append(data)
timestamps.extend(timestamp)
if inlet_marker:
marker, timestamp = inlet_marker.pull_sample(timeout=0.0)
if timestamp:
markers.append([marker, timestamp])
except KeyboardInterrupt:
break
time_correction = inlet.time_correction()
print('Time correction: ', time_correction)
res = np.concatenate(res, axis=0)
timestamps = np.array(timestamps) + time_correction
if dejitter:
y = timestamps
X = np.atleast_2d(np.arange(0, len(y))).T
lr = LinearRegression()
lr.fit(X, y)
timestamps = lr.predict(X)
res = np.c_[timestamps, res]
data = pd.DataFrame(data=res, columns=['timestamps'] + ch_names)
if inlet_marker:
n_markers = len(markers[0][0])
for ii in range(n_markers):
data['Marker%d' % ii] = 0
# process markers:
for marker in markers:
# find index of markers
ix = np.argmin(np.abs(marker[1] - timestamps))
for ii in range(n_markers):
data.loc[ix, 'Marker%d' % ii] = marker[0][ii]
directory = os.path.dirname(filename)
if not os.path.exists(directory):
os.makedirs(directory)
data.to_csv(filename, float_format='%.3f', index=False)
print('Done - wrote file: ' + filename + '.')
cap.append_child_value("name", "EasyCap")
cap.append_child_value("size", "54")
cap.append_child_value("labelscheme", "10-20")
# create outlet for the stream
outlet = StreamOutlet(info)
# (...normally here one might start sending data into the outlet...)
# === the following could run on another computer ===
# first we resolve a stream whose name is MetaTester (note that there are
# other ways to query a stream, too - for instance by content-type)
results = resolve_stream("name", "MetaTester")
# open an inlet so we can read the stream's data (and meta-data)
inlet = StreamInlet(results[0])
# get the full stream info (including custom meta-data) and dissect it
inf = inlet.info()
print("The stream's XML meta-data is: ")
print(inf.as_xml())
print("The manufacturer is: %s" % inf.desc().child_value("manufacturer"))
print("Cap circumference is: %s" % inf.desc().child("cap").child_value("size"))
print("The channel labels are as follows:")
ch = inf.desc().child("channels").child("channel")
for k in range(info.channel_count()):
print(" " + ch.child_value("label"))
ch = ch.next_sibling()
time.sleep(3)
def main():
inlet = None
timestamp_accumulated = None
data_accumulated = None
no_data_duration = None
# Create a outlet to relay the inference results ##################################################################
if len(
resolve_byprop(
'name', config.INFERENCE_LSL_RESULTS_NAME, timeout=0.5)) > 0:
print(
'Inference stream with name {0} alreayd exists, cannot start. Check if there are other same script running'
.format(config.INFERENCE_LSL_RESULTS_NAME))
raise Exception('Inference stream already exists')
lsl_data_type = config.INFERENCE_LSL_RESULTS_TYPE
lsl_data_name = config.INFERENCE_LSL_RESULTS_NAME
info = StreamInfo(lsl_data_name,
lsl_data_type,
channel_count=inference_chann_count,
channel_format='float32',
source_id=(''.join(
random.choice(string.digits) for i in range(8))),
nominal_srate=110)
info.desc().append_child_value("apocalyvec", "RealityNavigation")
chns = info.desc().append_child("inference")
channel_names = ['class' + str(i) for i in range(inference_chann_count)]
for label in channel_names:
ch = chns.append_child("channel")
ch.append_child_value("label", label)
outlet = StreamOutlet(info, max_buffered=360)
print('Created inference results out stream ...')
# Main Loop ##################################################################################
while True:
if inlet:
data, timestamp = inlet.pull_sample(timeout=1e-2)
if data:
no_data_duration = 0 # received data,
timestamp_accumulated.append(
timestamp) # accumulate timestamps
data_accumulated = np.concatenate(
[data_accumulated,
np.expand_dims(data, axis=-1)],
axis=-1) # take the most recent samples
# conduct inference
# simply take the tail of data
if data_accumulated.shape[-1] > f_data * time_window:
try:
_frame_min_max_adaptive = [
interp_negative(x) for x in data_accumulated[
use_channels,
-int(f_data * adaptive_min_max_time_window):]
]
data_downsampled_min = np.min(_frame_min_max_adaptive)
data_downsampled_max = np.max(_frame_min_max_adaptive)
except ValueError: # use default
data_downsampled_min = 2.
data_downsampled_max = 8.
samples = np.expand_dims(
data_accumulated[use_channels,
-int(f_data * time_window):],
axis=0)
samples_preprocessed = preprocess_eye_samples(
samples,
f_resample=f_resample,
data_downsampled_min=data_downsampled_min,
data_downsampled_max=data_downsampled_max)
results, _ = inference(samples_preprocessed)
# send the inference results via LSL, only send out the not-decoded results
outlet.push_sample(results)
inference_per_second = len(timestamp_accumulated) / (
timestamp_accumulated[-1] - timestamp_accumulated[0]) if timestamp_accumulated[-1] - \
timestamp_accumulated[
0] != 0. else 0.
print('Inference per second is ' +
str(inference_per_second),
end='\r',
flush=True)
else:
no_data_duration += time.time() - current_time
if no_data_duration > reconnect_patience:
print(
'No data seen on data stream with name {0}. Assume it is lost, trying to reconnect'
.format(data_stream_name))
inlet = None
else:
print('Waiting for data stream with name {0} ...'.format(
data_stream_name))
streams = resolve_byprop('name', data_stream_name)
if len(streams) < 0:
print('No stream found with name {0}, cannot start.'.format(
data_stream_name))
raise Exception('No stream found for given name')
# create a new inlet to read from the stream ######################################################################
inlet = StreamInlet(streams[0])
print('Found data stream with name {0}'.format(data_stream_name))
# model, y_encoder, data_downsampled_min, data_downsampled_max = load_model_params()
# data buffers #####################################################################
timestamp_accumulated = []
data_accumulated = np.empty(shape=(data_chann_count, 0))
no_data_duration = 0.
print('Entering inference loop')
current_time = time.time()
current_time = time.time()
def __init__(self,
stream,
fig,
axes,
window,
scale,
filter_data=True,
dejitter=True):
"""Init"""
self.stream = stream
self.window = window
self.scale = scale
self.dejitter = dejitter
self.inlet = StreamInlet(stream, max_chunklen=buf)
self.filt = filter_data
info = self.inlet.info()
description = info.desc()
self.sfreq = info.nominal_srate()
self.n_samples = int(self.sfreq * self.window)
self.n_chan = info.channel_count()
ch = description.child('channels').first_child()
ch_names = [ch.child_value('label')]
for i in range(self.n_chan):
ch = ch.next_sibling()
ch_names.append(ch.child_value('label'))
self.ch_names = ch_names
fig.canvas.mpl_connect('key_press_event', self.OnKeypress)
fig.canvas.mpl_connect('button_press_event', self.onclick)
self.fig = fig
self.axes = axes
sns.despine(left=True)
self.data = np.zeros((self.n_samples, self.n_chan))
self.times = np.arange(-self.window, 0, 1. / self.sfreq)
impedances = np.std(self.data, axis=0)
lines = []
for ii in range(self.n_chan):
line, = axes.plot(self.times[::subsample],
self.data[::subsample, ii] - ii,
lw=1)
lines.append(line)
self.lines = lines
axes.set_ylim(-self.n_chan + 0.5, 0.5)
ticks = np.arange(0, -self.n_chan, -1)
axes.set_xlabel('Time (s)')
axes.xaxis.grid(False)
axes.set_yticks(ticks)
ticks_labels = [
'%s - %.1f' % (ch_names[ii], impedances[ii])
for ii in range(self.n_chan)
]
axes.set_yticklabels(ticks_labels)
self.display_every = int(0.2 / (12 / self.sfreq))
# self.bf, self.af = butter(4, np.array([1, 40])/(self.sfreq/2.),
# 'bandpass')
self.bf = firwin(32,
np.array([1, 40]) / (self.sfreq / 2.),
width=0.05,
pass_zero=False)
self.af = [1.0]
zi = lfilter_zi(self.bf, self.af)
self.filt_state = np.tile(zi, (self.n_chan, 1)).transpose()
self.data_f = np.zeros((self.n_samples, self.n_chan))
INDEX_CHANNEL_JAW = [2]
INDEX_CHANNELS = [INDEX_CHANNEL_BLINK, INDEX_CHANNEL_JAW]
if __name__ == "__main__":
""" 1. CONNECT TO EEG STREAM """
# Search for active LSL streams
print('Looking for an EEG stream...')
streams = resolve_byprop('type', 'EEG', timeout=2)
if len(streams) == 0:
raise RuntimeError('Can\'t find EEG stream.')
# Set active EEG stream to inlet and apply time correction
print("Start acquiring data")
inlet = StreamInlet(streams[0], max_chunklen=12)
# Get the stream info
info = inlet.info()
# Get the sampling frequency
# This is an important value that represents how many EEG data points are
# collected in a second. This influences our frequency band calculation.
# for the Muse 2016, this should always be 256
fs = int(info.nominal_srate())
""" 2. INITIALIZE BUFFERS """
# Initialize raw EEG data buffer
eeg_buffer = np.zeros((int(fs * BUFFER_LENGTH), 1))
filter_state = None # for use with the notch filter
def ReceiveAndDisplayEEG(self, lowfreq=1, highfreq=81, band=[10, 20]):
if self.currentObj is None:
print("no mesh")
return
try:
invMat = scipy.io.loadmat(
'/home/neuropsynov/hugHackathon/Manik_actiCHamp64_5000.mat')
except:
print("can't open the inv mat")
return
try:
spi2gii = np.load('/home/neuropsynov/hugHackathon/spi2fullGii.npy')
except:
print("can't open the spi2gii file")
return
print("looking for an EEG stream...")
streams = resolve_byprop('type', 'EEG', timeout=5.0)
if len(streams) == 0:
print("no eeg signal found")
return
#check number of contact match invMat size
if streams[0].channel_count() != invMat["x"].shape[1]:
print("invMat and channel_count doesn't match")
#check if we have EOG for eye artefact removal
#prepare the texture object
if self.aimsMesh is None:
self.aimsMesh = self.a.toAimsObject(self.mesh)
self.texture[0].assign([0] * len(self.aimsMesh.vertex()))
currentIndex = 0
buffSize = 500
buff = np.zeros([buffSize, streams[0].channel_count()])
#est-ce qu'on fait un buffer "normalisation" avec le signal en degageant du signal tout ce qui est >5 la MAD ?
self.EEGAnalysisParam = {}
nyq = 0.5 * streams[0].nominal_srate()
low = lowfreq / nyq
high = highfreq / nyq
paramFil = scipy.signal.butter(6, [low, high], btype='band')
# create a new inlet to read from the stream
inlet = StreamInlet(streams[0])
self.mustStop = False
self.TextObj.setPalette(palette='Blue-Red')
while not self.mustStop:
print(currentIndex)
np.roll(buff, -1, 0)
buff[buffSize - 1, :], timestamp = inlet.pull_sample()
if currentIndex >= 500:
filtSample = scipy.signal.filtfilt(paramFil[0],
paramFil[1],
buff,
method="gust",
axis=0)
#il faudrait faire une fastICA ici
#on prend juste le milieu du signal ?
f_welch, S_xx_welch = scipy.signal.welch(
filtSample[math.floor(buffSize /
4):math.floor(buffSize / 4) * 3, :],
fs=streams[0].nominal_srate(),
nfft=math.floor(2 * streams[0].nominal_srate() / 2.5),
nperseg=math.floor(streams[0].nominal_srate() / 5.0),
axis=0,
scaling="density")
freqKept = (f_welch >= band[0]) & (f_welch <= band[1])
DataToProject = np.mean(S_xx_welch[freqKept, :], axis=0)
#DataToProject = buff[250,:]
invsolmat = np.sqrt(
np.multiply(np.dot(invMat["x"], DataToProject),
np.dot(invMat["x"], DataToProject)) +
np.multiply(np.dot(invMat["y"], DataToProject),
np.dot(invMat["y"], DataToProject)) +
np.multiply(np.dot(invMat["z"], DataToProject),
np.dot(invMat["z"], DataToProject)))
#if currentIndex % 500 == 0:
# plt.plot(filtSample[:,5], 'b-')
# plt.plot(buff[:,5], 'r-')
# plt.show()
fullTexture = np.dot(spi2gii, invsolmat)
self.texture2[0].assign(fullTexture)
self.TextObj.setTexture(self.texture2, True)
self.TextObj.notifyObservers()
self.app.processEvents()
#mapping avec les leds
currentIndex += 1
pdb.set_trace()
def create_streams(self, recursion_meter=0, max_recursion_depth=3):
'''
Opens two LSL streams: one for EEG, another for markers, If error, tries to reconnect several times
'''
if self.device == 'Enobio':
stream_type_eeg = 'EEG'
stream_name_markers = 'CycleStart'
elif self.device == 'NVX52':
stream_type_eeg = 'Data'
stream_name_markers = 'CycleStart'
stream_name_events = 'NVX52_Events'
STREAM_NVX_PHOTOCELL = True
else:
print 'I don\'t know device {}!s\n\n\nWill now exit!'.format(
self.device)
sys.exit()
if recursion_meter == 0:
recursion_meter += 1
elif 0 < recursion_meter < max_recursion_depth:
print 'Trying to reconnect for the %i time \n' % (recursion_meter +
1)
recursion_meter += 1
else:
print 'exiting'
sys.exit()
inlet_eeg = []
inlet_markers = []
if self.StreamEeg == True:
print("Connecting to %s stream..." % self.device)
if stream_type_eeg in [
stream.type() for stream in resolve_stream()
]:
streams_eeg = resolve_stream('type', stream_type_eeg)
inlet_eeg = StreamInlet(streams_eeg[0])
try:
inlet_eeg
self.sampling_rate = streams_eeg[0].nominal_srate()
# print inlet_eeg.info().as_xml()
print '...done \n'
except NameError:
print("Error: Cannot conect to %s stream\n" % self.device)
sys.exit()
else:
print 'Error: %s stream is not available\n' % self.device
sys.exit()
else:
inlet_eeg = []
if STREAM_NVX_PHOTOCELL == True:
print("Connecting to %s photocell stream..." % self.device)
if stream_name_events in [
stream.name() for stream in resolve_stream()
]:
streams_eeg = resolve_stream('name', stream_name_events)
inlet_photocell = StreamInlet(streams_eeg[0])
try:
inlet_photocell
# print inlet_eeg.info().as_xml()
print '...done \n'
except NameError:
print("Error: Cannot conect to %s photocell stream\n" %
self.device)
sys.exit()
else:
print 'Error: %s stream is not available\n' % self.device
sys.exit()
else:
inlet_photocell = []
if self.StreamMarkers == True:
print("Connecting to Psychopy stream...")
if stream_name_markers in [
stream.name() for stream in resolve_stream()
]:
sterams_markers = resolve_stream('name', stream_name_markers)
inlet_markers = StreamInlet(sterams_markers[0])
try:
inlet_markers
print '...done \n'
except NameError:
print("Error: Cannot conect to Psychopy stream\n")
sys.exit()
else:
print 'Error: Psychopy stream is not available\n'
return self.create_streams(stream_type_eeg,
stream_name_markers, StreamEeg,
StreamMarkers, recursion_meter)
else:
inlet_markers = []
return inlet_eeg, inlet_markers, inlet_photocell
from collections import deque
import os
import random
ACTION = 'idle'
FFT_MAX_HZ = 60
HM_SECONDS = 10 # this is approximate. Not 100%. do not depend on this.
TOTAL_ITERS = HM_SECONDS * 25 # ~25 iters/sec
last_print = time.time()
fps_counter = deque(maxlen=150)
# first resolve an EEG stream on the lab network
print("looking for an EEG stream...")
streams = resolve_stream('type', 'EEG')
# create a new inlet to read from the stream
inlet = StreamInlet(streams[-1])
total = 0
left = 0
right = 0
none = 0
correct = 0
channel_datas = []
for i in range(TOTAL_ITERS
): # how many iterations. Eventually this would be a while True
channel_data = []
for i in range(60):
sample, timestamp = inlet.pull_sample()
channel_data.append(sample[:FFT_MAX_HZ])
fps_counter.append(time.time() - last_print)
last_print = time.time()
cur_raw_hz = 1 / (sum(fps_counter) / len(fps_counter))
from threading import Thread
# Enough for 1 sec at 256 Hz
BUFFER = 256
print("looking for an EEG stream...")
streams = resolve_byprop('type', 'EEG', timeout=2)
if len(streams) == 0:
raise(RuntimeError("Cant find EEG stream"))
print("Start aquiring data")
stream = streams[0]
inlet = StreamInlet(stream, max_chunklen=BUFFER)
def alpha(data):
#data.filter(8, 12, n_jobs=1, l_trans_bandwidth=1, h_trans_bandwidth=1, fir_design='firwin')
#data.apply_hilbert(n_jobs=1, envelope=False)
#epochs = mne.Epochs(data, events, 1, -1.0, 3.0, baseline=None, reject=dict(grad=4000e-13, eog=350e-6))
return np.average([math.sqrt(part.real**2 + part.imag**2) for part in data])
class CircularBuffer:
def __init__(self, chunks):
self.window = np.zeros((5, 256*chunks))
self.chunks = chunks
self.window_read = self.chunks // 2
self.window_write = 0
import win32api
def doSaneThing(sig, func=None):
print("END")
Drone.send('end')
raise KeyboardInterrupt
if __name__ == "__main__":
#%% catching the control-C event
win32api.SetConsoleCtrlHandler(doSaneThing, 1)
#%% Access stream
streams = resolve_stream('type', 'EEG')
inlet = StreamInlet(streams[0], max_buflen=1000)
info = inlet.info()
NAME = inlet.info().name()
SAMPLE_RATE = int(info.nominal_srate())
CHANNEL_COUNT = int(info.channel_count())
print("Name: {:s}".format(NAME))
print("Channel Count: {:d}".format(CHANNEL_COUNT))
print("Sample Rate: {:d}".format(SAMPLE_RATE))
#%% Initialize of the Drone interface
Drone = TELLO()
#List of stimulus frequencies
list_freqs = np.arange(8.0, 13.0 + 1, 1)
COMMAND = [
'takeoff', 'land', 'forward 10', 'back 10', 'left 10', 'right 10'
samplingRate = 256
numSensors = 4
dataBuffer = np.zeros([numSensors, windowLength])
# api parser
parser = argparse.ArgumentParser()
parser.add_argument('-k',
'--api_key',
type=str,
required=True,
help='API key for the Petal Metrics API')
args = parser.parse_args()
print("looking for an EEG stream...")
streams = resolve_stream('type', 'EEG')
inlet = StreamInlet(streams[0])
while True:
sample, timestamp = inlet.pull_chunk(timeout=2, max_samples=chunkLength)
sample = np.asarray(sample).T
for channel in range(0, numSensors):
dataBuffer[channel] = np.roll(dataBuffer[channel], -chunkLength)
for newSample in range(0, chunkLength):
dataBuffer[channel][len(dataBuffer[channel]) - chunkLength +
newSample] = sample[channel][newSample]
finalDataAPI = np.asarray(dataBuffer).tolist()
#call api
apiOutput = request_metrics(
api_key=args.api_key,
eeg_data=finalDataAPI,
def plotNodes(i):
# define all global variables
global data
global globalMax
global line1
global line2
# Create an inlet
inlet = StreamInlet(streams[0])
# Pull data into the inlet
amplitudes = inlet.pull_sample()
# create a new numpy array called new_data for amplitudes
new_data = np.asarray(amplitudes[0][:n])
# get the frequency via delta band or by accessing individual frequencies
temp, localMax, data = getFreqBandOrValue(data, new_data, 0, globalMax)
# Initialize row and column dimensions for the 64 x 64 list
# initialize row dimension to 64
i = 64
# initialize column dimension to 64
j = 64
# Create and initialize a 2-dimensional list with zeros to store differences between amplitudes
differences = [[0] * j for i in range(64)]
# Compute the differences between the amplitudes at every given xth, yth position
for x in range(63):
for y in range(63):
# Compute the difference between the amplitudes at every given xth, yth position
difference = abs(temp[x] - temp[y])
# Store the current difference in the differences list at the given xth, yth position
differences[x][y] = difference
# Convert the differences list into a (64 x 64)-element Numpy array
differences_data = np.asarray(differences)
# print the shape/size of the differences_data array
print(np.shape(differences_data))
# Compute the z-score of the differences
z_scores = np.zeros([64, 64])
# print the shape/size of the z-scores array
print(np.shape(z_scores))
# calculate the z-scores for each row and column in the differences_data array
for i in range(63):
# calculate the z-scores
z_scores[:n][i] = stats.zscore(differences_data[:n][i])
# print the z-scores
print(z_scores[:n][i])
# set the x-axis limits for plot ax1
ax1.set_xlim(-6, 6)
# set the y-axis limits for plot ax1
ax1.set_ylim(-6, 6)
# Plot z-scores of differences in amplitudes
ax1.scatter(x, y)
# Plot a red line between electrodes where z-score > 2.3
# Plot a blue line between electrodes where z-score < -2.3
# Dimensions of z-scores numpy array is not acceptable in scatter function
# remove every line in ax1
for line in ax1.lines:
line.remove()
# for every pair of electrodes:
# - plot a red line where the z-scores of the differences is > 2.3
# - plot a blue line where the z-scores of the differences is < -2.3
for i in range(64):
for j in range(64):
if j <= i:
continue
# if z-scores is > 2.3, plot a red line on ax1
if z_scores[i, j] > 2.3:
Red_Lines(list[i], list[j], ax1)
# if z-scores is < -2.3, plot a blue line on ax1
elif z_scores[i, j] < -2.3:
Blue_Lines(list[i], list[j], ax1)
# else, continue
else:
continue