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lsl-record_comb.py
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lsl-record_comb.py
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#!/usr/bin/env python
import numpy as np
import os
import pandas as pd
from time import time, strftime, gmtime
from optparse import OptionParser
from pylsl import StreamInlet, resolve_byprop
from sklearn.linear_model import LinearRegression
partnum = "001"
stop_eeg = [0]
np.savetxt("/home/pi/Experiments/Flanker/Stop_EEG2.csv", (stop_eeg), delimiter=',',fmt="%s")
while os.path.isfile("/home/pi/Experiments/Flanker/Data/Amp/LSL_Data/%s_comb_lsl_data_flanker.csv"%(partnum)) == True:
partnum = '00'+str(int(partnum)+1)
if int(partnum) > 9:
partnum = '0' + str(int(partnum))
filename = "%s_comb_lsl_data_flanker"%(partnum)
default_fname = ("/home/pi/Experiments/Flanker/Data/Amp/LSL_Data/%s.csv" % filename)
parser = OptionParser()
parser.add_option("-d", "--duration",
dest="duration", type='int', default=60,
help="duration of the recording in seconds.")
parser.add_option("-f", "--filename",
dest="filename", type='str', default=default_fname,
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=15)
if len(streams) == 0:
raise(RuntimeError, "Cant find EEG stream")
print("Start aquiring 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=60)
if marker_streams:
inlet_marker = StreamInlet(marker_streams[0])
marker_time_correction = inlet_marker.time_correction()
else:
inlet_marker = False
print("Cant find Markers stream")
info = inlet.info()
description = info.desc()
freq = info.nominal_srate()
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(time_correction)
print('Start recording at time t=%.3f' % t_init)
while os.path.isfile("/home/pi/Experiments/Flanker/Stop_EEG2.csv") == True:
try:
data, timestamp = inlet.pull_chunk(timeout=1.0,
max_samples=12)
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)
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)
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 margers
ix = np.argmin(np.abs(marker[1] - timestamps))
val = timestamps[ix]
for ii in range(n_markers):
data.loc[ix, 'Marker%d' % ii] = marker[0][ii]
data.to_csv(options.filename, float_format='%.3f', index=False)
print('Done !')