add lab 4 materials
Esse commit está contido em:
Pierre Karashchuk
@@ -87,3 +87,6 @@ ENV/
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# Rope project settings
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.ropeproject
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## node stuff
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node_modules
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@@ -0,0 +1,16 @@
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## code by Alexandre Barachant
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## adapted by Pierre Karashchuk for compatibility with Python3
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from pylsl import StreamInfo, StreamOutlet
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import numpy as np
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from builtins import input
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info = StreamInfo('Ganglion_EEG', 'EEG', 4, 200, 'float32',
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'Ganglion_123456789')
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outlet = StreamOutlet(info)
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while True:
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strSample = input().split(': ', 1)
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sample = 1e6*np.array(list(map(float, strSample[1].split(' '))))
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stamp = float(strSample[0])*1e-3
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outlet.push_sample(sample, stamp)
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# print('Pushed Sample At: ' + strSample[0])
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@@ -0,0 +1,27 @@
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# Lab 3: Event Related Potentials
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### Introduction
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In this lab, we will record EEG while trying to remember words, as well as later recognizing these same words among others. Hopefully, we'll be able to see the event related potentials corresponding to remembered vs not-remembered words, and possibly recognized vs not recognized words.
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### Setup
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First, install the libraries:
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```
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npm install
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pip install -r requirements.txt
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```
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(If you don't have `npm`, you can install by running `brew install node`. You can get `brew` from https://brew.sh/)
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### Stimulus Presentation + Recording
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- Attach Ganglion to participant's head.
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- Record positions of EEG according to 10-20 system.
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- Have participant sit in chair in front of monitor
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- Connect to the ganglion and stream data: `node ganglion-lsl.js`
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- Run lsl-viewer to check connections and stream: `python lsl-viewer.py`
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- Start presentation list of words: `cd paradigm; python encode.py`
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- Start recording data (in separate terminal): `python lsl-record.py`
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- Press space to start presentation
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- Finally, start recall Procedure: `cd paradigm; python recognize.py ../data/words_latest.csv`
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@@ -0,0 +1,29 @@
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// code by Alexandre Barachant
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const Ganglion = require('openbci-ganglion').Ganglion;
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const ganglion = new Ganglion();
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// Construct LSL Handoff Python Shell
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var PythonShell = require('python-shell');
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var lsloutlet = new PythonShell('LSLHandoff.py');
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lsloutlet.on('message', function(message){
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console.log('LslOutlet: ' + message);
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});
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console.log('Python Shell Created for LSLHandoff');
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ganglion.once('ganglionFound', (peripheral) => {
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// Stop searching for BLE devices once a ganglion is found.
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ganglion.searchStop();
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ganglion.on('sample', (sample) => {
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/** Work with sample */
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st = sample.channelData.join(' ');
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var s = ''+ sample.timeStamp + ': '+ st
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lsloutlet.send(s)
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});
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ganglion.once('ready', () => {
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ganglion.streamStart();
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});
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ganglion.connect(peripheral);
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});
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// Start scanning for BLE devices
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ganglion.searchStart();
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Arquivo executável
+101
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#!/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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Arquivo executável
+191
@@ -0,0 +1,191 @@
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#!/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 matplotlib.pyplot as plt
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from scipy.signal import butter, filtfilt
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from time import time, sleep
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from pylsl import StreamInlet, resolve_byprop
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import seaborn as sns
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from threading import Thread
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sns.set(style="whitegrid")
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from optparse import OptionParser
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parser = OptionParser()
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parser.add_option("-w", "--window",
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dest="window", type='float', default=5.,
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help="window lenght to display in seconds.")
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parser.add_option("-s", "--scale",
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dest="scale", type='float', default=100,
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help="scale in uV")
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parser.add_option("-r", "--refresh",
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dest="refresh", type='float', default=0.2,
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help="refresh rate in seconds.")
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parser.add_option("-f", "--figure",
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dest="figure", type='string', default="15x6",
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help="window size.")
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filt = True
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subsample = 2
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buf = 12
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(options, args) = parser.parse_args()
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window = options.window
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scale = options.scale
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figsize = np.int16(options.figure.split('x'))
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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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class LSLViewer():
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def __init__(self, stream, fig, axes, window, scale, dejitter=True):
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"""Init"""
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self.stream = stream
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self.window = window
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self.scale = scale
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self.dejitter = dejitter
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self.inlet = StreamInlet(stream, max_chunklen=buf)
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self.filt = True
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info = self.inlet.info()
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description = info.desc()
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self.sfreq = info.nominal_srate()
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self.n_samples = int(self.sfreq * self.window)
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self.n_chan = 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(self.n_chan):
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ch = ch.next_sibling()
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ch_names.append(ch.child_value('label'))
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self.ch_names = ch_names
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fig.canvas.mpl_connect('key_press_event', self.OnKeypress)
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fig.canvas.mpl_connect('button_press_event', self.onclick)
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self.fig = fig
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self.axes = axes
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sns.despine(left=True)
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self.data = np.zeros((self.n_samples, self.n_chan))
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self.times = np.arange(-self.window, 0, 1./self.sfreq)
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impedances = np.std(self.data, axis=0)
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lines = []
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for ii in range(self.n_chan):
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line, = axes.plot(self.times[::subsample],
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self.data[::subsample, ii] - ii, lw=1)
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lines.append(line)
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self.lines = lines
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axes.set_ylim(-self.n_chan + 0.5, 0.5)
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ticks = np.arange(0, -self.n_chan, -1)
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axes.set_xlabel('Time (s)')
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axes.xaxis.grid(False)
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axes.set_yticks(ticks)
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ticks_labels = ['%s - %.1f' % (ch_names[ii], impedances[ii])
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for ii in range(self.n_chan)]
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axes.set_yticklabels(ticks_labels)
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self.display_every = int(0.2 / (12/self.sfreq))
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self.bf, self.af = butter(4, np.array([1, 40])/(self.sfreq/2.),
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'bandpass')
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def update_plot(self):
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k = 0
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while self.started:
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samples, timestamps = self.inlet.pull_chunk(timeout=1.0,
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max_samples=12)
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if timestamps:
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self.data = np.vstack([self.data, samples])
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if self.dejitter:
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timestamps = np.float64(np.arange(len(timestamps)))
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timestamps /= self.sfreq
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timestamps += self.times[-1] + 1./self.sfreq
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self.times = np.concatenate([self.times, timestamps])
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self.n_samples = int(self.sfreq * self.window)
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self.data = self.data[-self.n_samples:]
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self.times = self.times[-self.n_samples:]
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k += 1
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if k == self.display_every:
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if self.filt:
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data_f = filtfilt(self.bf, self.af, self.data, axis=0)
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else:
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data_f = self.data
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data_f -= data_f.mean(axis=0)
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for ii in range(self.n_chan):
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self.lines[ii].set_xdata(self.times[::subsample] -
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self.times[-1])
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self.lines[ii].set_ydata(data_f[::subsample, ii] /
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self.scale - ii)
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impedances = np.std(data_f, axis=0)
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ticks_labels = ['%s - %.2f' %
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(self.ch_names[ii], impedances[ii])
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for ii in range(self.n_chan)]
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self.axes.set_yticklabels(ticks_labels)
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self.axes.set_xlim(-self.window, 0)
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self.fig.canvas.draw()
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k = 0
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else:
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sleep(0.2)
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def onclick(self, event):
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print((event.button, event.x, event.y, event.xdata, event.ydata))
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def OnKeypress(self, event):
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if event.key == '/':
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self.scale *= 1.2
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elif event.key == '*':
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self.scale /= 1.2
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elif event.key == '+':
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self.window += 1
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elif event.key == '-':
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if self.window > 1:
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self.window -= 1
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elif event.key == 'd':
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self.filt = not(self.filt)
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def start(self):
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self.started = True
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self.thread = Thread(target=self.update_plot)
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self.thread.daemon = True
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self.thread.start()
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def stop(self):
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self.started = False
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fig, axes = plt.subplots(1, 1, figsize=figsize, sharex=True)
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lslv = LSLViewer(streams[0], fig, axes, window, scale)
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help_str = """
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toggle filter : d
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toogle full screen : f
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zoom out : /
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zoom in : *
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increase time scale : -
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decrease time scale : +
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"""
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print(help_str)
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lslv.start()
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plt.show()
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lslv.stop()
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Arquivo executável
+211
@@ -0,0 +1,211 @@
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#!/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 matplotlib.pyplot as plt
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from scipy.signal import butter, filtfilt
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from time import time, sleep
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from pylsl import StreamInlet, resolve_byprop
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import seaborn as sns
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from threading import Thread
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from scipy import signal
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sns.set(style="whitegrid")
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from optparse import OptionParser
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parser = OptionParser()
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parser.add_option("-w", "--window",
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dest="window", type='float', default=0.5,
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help="window lenght to display in seconds.")
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parser.add_option("-s", "--scale",
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dest="scale", type='float', default=100,
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help="scale in uV")
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parser.add_option("-r", "--refresh",
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dest="refresh", type='float', default=0.2,
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help="refresh rate in seconds.")
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parser.add_option("-f", "--figure",
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dest="figure", type='string', default="15x6",
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help="window size.")
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filt = True
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subsample = 2
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buf = 12
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(options, args) = parser.parse_args()
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window = options.window
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scale = options.scale
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figsize = np.int16(options.figure.split('x'))
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refresh = options.refresh
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decrease_fs = [4, 8]
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increase_fs = [12, 20]
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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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class LSLViewer():
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def __init__(self, stream, fig, axes, window, scale, dejitter=True):
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"""Init"""
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self.stream = stream
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self.window = window
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self.scale = scale
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self.dejitter = dejitter
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self.inlet = StreamInlet(stream, max_chunklen=buf)
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self.filt = True
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info = self.inlet.info()
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description = info.desc()
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self.sfreq = info.nominal_srate()
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self.n_samples = int(self.sfreq * self.window)
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self.n_chan = 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(self.n_chan):
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ch = ch.next_sibling()
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ch_names.append(ch.child_value('label'))
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self.ch_names = ch_names
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fig.canvas.mpl_connect('key_press_event', self.OnKeypress)
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fig.canvas.mpl_connect('button_press_event', self.onclick)
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self.fig = fig
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self.axes = axes
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sns.despine(left=True)
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self.data = np.zeros((self.n_samples, self.n_chan))
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self.times = np.arange(-self.window, 0, 1./self.sfreq)
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impedances = np.std(self.data, axis=0)
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lines = []
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self.rects = axes.bar(0, 1)
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# self.text = axes.
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axes.xaxis.grid(False)
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axes.set_xticks([])
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self.value = None
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self.display_every = int(refresh / (12/self.sfreq))
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self.bf1, self.af1 = butter(4, np.array(decrease_fs)/(self.sfreq/2.),
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'bandpass')
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self.bf2, self.af2 = butter(4, np.array(increase_fs)/(self.sfreq/2.),
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'bandpass')
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self.low = 10000
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||||
self.high = 0
|
||||
|
||||
def compute_value(self):
|
||||
data_f1 = filtfilt(self.bf1, self.af1, self.data, axis=0)
|
||||
data_f2 = filtfilt(self.bf2, self.af2, self.data, axis=0)
|
||||
|
||||
v1 = np.sqrt(np.sum(np.square(data_f1)))
|
||||
v2 = np.sqrt(np.sum(np.square(data_f2)))
|
||||
|
||||
return v2 / v1
|
||||
|
||||
|
||||
def update_plot(self):
|
||||
value = self.compute_value()
|
||||
|
||||
if self.value is None:
|
||||
self.value = value
|
||||
|
||||
self.value = 0.8 * self.value + 0.2 * value
|
||||
|
||||
self.low = min(self.low, self.value)
|
||||
self.high = max(self.high, self.value)
|
||||
|
||||
rect = self.rects.get_children()[0]
|
||||
rect.set_height(self.value)
|
||||
|
||||
self.axes.set_ylim([self.low, self.high])
|
||||
self.fig.canvas.draw()
|
||||
plt.pause(0.01)
|
||||
|
||||
|
||||
def update_data_and_plot(self):
|
||||
k = 0
|
||||
while self.started:
|
||||
samples, timestamps = self.inlet.pull_chunk(timeout=1.0,
|
||||
max_samples=buf)
|
||||
|
||||
if timestamps:
|
||||
self.data = np.vstack([self.data, samples])
|
||||
if self.dejitter:
|
||||
timestamps = np.float64(np.arange(len(timestamps)))
|
||||
timestamps /= self.sfreq
|
||||
timestamps += self.times[-1] + 1./self.sfreq
|
||||
self.times = np.concatenate([self.times, timestamps])
|
||||
|
||||
self.n_samples = int(self.sfreq * self.window)
|
||||
self.data = self.data[-self.n_samples:]
|
||||
self.times = self.times[-self.n_samples:]
|
||||
|
||||
|
||||
k += 1
|
||||
|
||||
if k >= self.display_every:
|
||||
self.update_plot()
|
||||
k = 0
|
||||
else:
|
||||
sleep(0.1)
|
||||
|
||||
def onclick(self, event):
|
||||
print((event.button, event.x, event.y, event.xdata, event.ydata))
|
||||
|
||||
def OnKeypress(self, event):
|
||||
if event.key == 'r':
|
||||
self.low = 10000
|
||||
self.high = 0
|
||||
elif event.key == '+':
|
||||
self.window += 1
|
||||
elif event.key == '-':
|
||||
if self.window > 1:
|
||||
self.window -= 1
|
||||
elif event.key == 'd':
|
||||
self.filt = not(self.filt)
|
||||
|
||||
def start(self):
|
||||
self.started = True
|
||||
self.thread = Thread(target=self.update_data_and_plot)
|
||||
self.thread.daemon = True
|
||||
self.thread.start()
|
||||
|
||||
def stop(self):
|
||||
self.started = False
|
||||
|
||||
|
||||
fig, axes = plt.subplots(1, 1, figsize=figsize, sharex=True)
|
||||
lslv = LSLViewer(streams[0], fig, axes, window, scale)
|
||||
|
||||
help_str = """
|
||||
reset scale: r
|
||||
increase time scale : -
|
||||
decrease time scale : +
|
||||
"""
|
||||
print(help_str)
|
||||
lslv.start()
|
||||
|
||||
plt.show()
|
||||
lslv.stop()
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"name": "lab3",
|
||||
"version": "1.0.0",
|
||||
"description": "",
|
||||
"main": "ganglion-lsl.js",
|
||||
"dependencies": {
|
||||
"openbci-ganglion": "^0.4.3",
|
||||
"python-shell": "^0.4.0"
|
||||
},
|
||||
"devDependencies": {},
|
||||
"scripts": {
|
||||
"test": "echo \"Error: no test specified\" && exit 1"
|
||||
},
|
||||
"author": "",
|
||||
"license": "GPL-3.0"
|
||||
}
|
||||
@@ -0,0 +1,8 @@
|
||||
pygame
|
||||
numpy
|
||||
scipy
|
||||
matplotlib
|
||||
pylsl
|
||||
pandas
|
||||
scikit-learn
|
||||
seaborn
|
||||
Arquivo executável
+43
@@ -0,0 +1,43 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
N = 5
|
||||
men_means = (20, 35, 30, 35, 27)
|
||||
men_std = (2, 3, 4, 1, 2)
|
||||
|
||||
ind = np.arange(N) # the x locations for the groups
|
||||
width = 0.35 # the width of the bars
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
rects1 = ax.bar(0, 10, width, color='r')
|
||||
|
||||
# women_means = (25, 32, 34, 20, 25)
|
||||
# women_std = (3, 5, 2, 3, 3)
|
||||
# rects2 = ax.bar(ind + width, women_means, width,
|
||||
# color='y', yerr=women_std)
|
||||
|
||||
# add some text for labels, title and axes ticks
|
||||
ax.set_ylabel('Scores')
|
||||
ax.set_title('Scores by group and gender')
|
||||
# ax.set_xticks(ind + width / 2)
|
||||
ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
|
||||
|
||||
# ax.legend((rects1[0], rects2[0]), ('Men', 'Women'))
|
||||
|
||||
|
||||
def autolabel(rects):
|
||||
"""
|
||||
Attach a text label above each bar displaying its height
|
||||
"""
|
||||
for rect in rects:
|
||||
height = rect.get_height()
|
||||
ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,
|
||||
'%d' % int(height),
|
||||
ha='center', va='bottom')
|
||||
|
||||
# autolabel(rects1)
|
||||
# autolabel(rects2)
|
||||
|
||||
plt.show(block=False)
|
||||
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