Pierre Karashchuk
102 linhas
2.8 KiB
Python
Arquivo Executável
102 linhas
2.8 KiB
Python
Arquivo Executável
#!/usr/bin/env python
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## code by Alexandre Barachant
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import numpy as np
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import pandas as pd
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from time import time, strftime, gmtime
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from optparse import OptionParser
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from pylsl import StreamInlet, resolve_byprop
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from sklearn.linear_model import LinearRegression
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default_fname = ("data/data_%s.csv" % strftime("%Y-%m-%d-%H.%M.%S", gmtime()))
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parser = OptionParser()
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parser.add_option("-d", "--duration",
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dest="duration", type='int', default=300,
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help="duration of the recording in seconds.")
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parser.add_option("-f", "--filename",
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dest="filename", type='str', default=default_fname,
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help="Name of the recording file.")
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# dejitter timestamps
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dejitter = False
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(options, args) = parser.parse_args()
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print("looking for an EEG stream...")
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streams = resolve_byprop('type', 'EEG', timeout=2)
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if len(streams) == 0:
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raise(RuntimeError, "Cant find EEG stream")
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print("Start aquiring data")
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inlet = StreamInlet(streams[0], max_chunklen=12)
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eeg_time_correction = inlet.time_correction()
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print("looking for a Markers stream...")
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marker_streams = resolve_byprop('type', 'Markers', timeout=2)
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if marker_streams:
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inlet_marker = StreamInlet(marker_streams[0])
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marker_time_correction = inlet_marker.time_correction()
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else:
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inlet_marker = False
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print("Cant find Markers stream")
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info = inlet.info()
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description = info.desc()
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freq = info.nominal_srate()
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Nchan = info.channel_count()
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ch = description.child('channels').first_child()
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ch_names = [ch.child_value('label')]
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for i in range(1, Nchan):
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ch = ch.next_sibling()
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ch_names.append(ch.child_value('label'))
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res = []
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timestamps = []
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markers = []
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t_init = time()
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print('Start recording at time t=%.3f' % t_init)
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while (time() - t_init) < options.duration:
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try:
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data, timestamp = inlet.pull_chunk(timeout=1.0,
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max_samples=12)
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if timestamp:
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res.append(data)
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timestamps.extend(timestamp)
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if inlet_marker:
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marker, timestamp = inlet_marker.pull_sample(timeout=0.0)
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if timestamp:
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markers.append([marker, timestamp])
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except KeyboardInterrupt:
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break
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res = np.concatenate(res, axis=0)
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timestamps = np.array(timestamps)
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if dejitter:
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y = timestamps
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X = np.atleast_2d(np.arange(0, len(y))).T
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lr = LinearRegression()
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lr.fit(X, y)
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timestamps = lr.predict(X)
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res = np.c_[timestamps, res]
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data = pd.DataFrame(data=res, columns=['timestamps'] + ch_names)
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data['Marker'] = 0
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# process markers:
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for marker in markers:
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# find index of margers
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ix = np.argmin(np.abs(marker[1] - timestamps))
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val = timestamps[ix]
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data.loc[ix, 'Marker'] = marker[0][0]
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data.to_csv(options.filename, float_format='%.3f', index=False)
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print('Done !')
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