From e8551ff81cb0e4222bd1fe3dc969a19586b112e0 Mon Sep 17 00:00:00 2001 From: Pierre Karashchuk Date: Tue, 7 Mar 2017 15:46:41 -0800 Subject: [PATCH] add lab 5 and update code to be more python 2 compatible --- lab3/LSLHandoff.py | 5 +- lab4/LSLHandoff.py | 5 +- lab5/Detecting Ashton's SSVEP.ipynb | 476 ++++++++++++++++++++++++++++ lab5/LSLHandoff.py | 19 ++ lab5/README.md | 37 +++ lab5/ganglion-lsl.js | 29 ++ lab5/lsl-record.py | 101 ++++++ lab5/lsl-viewer.py | 191 +++++++++++ lab5/package.json | 16 + lab5/requirements.txt | 6 + lab5/stimulus.py | 94 ++++++ 11 files changed, 977 insertions(+), 2 deletions(-) create mode 100644 lab5/Detecting Ashton's SSVEP.ipynb create mode 100644 lab5/LSLHandoff.py create mode 100644 lab5/README.md create mode 100644 lab5/ganglion-lsl.js create mode 100755 lab5/lsl-record.py create mode 100755 lab5/lsl-viewer.py create mode 100644 lab5/package.json create mode 100644 lab5/requirements.txt create mode 100755 lab5/stimulus.py diff --git a/lab3/LSLHandoff.py b/lab3/LSLHandoff.py index 6f37d80..dcdabc3 100644 --- a/lab3/LSLHandoff.py +++ b/lab3/LSLHandoff.py @@ -3,7 +3,10 @@ from pylsl import StreamInfo, StreamOutlet import numpy as np -from builtins import input +try: + input = raw_input +except NameError: + pass info = StreamInfo('Ganglion_EEG', 'EEG', 4, 200, 'float32', 'Ganglion_123456789') diff --git a/lab4/LSLHandoff.py b/lab4/LSLHandoff.py index 6f37d80..dcdabc3 100644 --- a/lab4/LSLHandoff.py +++ b/lab4/LSLHandoff.py @@ -3,7 +3,10 @@ from pylsl import StreamInfo, StreamOutlet import numpy as np -from builtins import input +try: + input = raw_input +except NameError: + pass info = StreamInfo('Ganglion_EEG', 'EEG', 4, 200, 'float32', 'Ganglion_123456789') diff --git a/lab5/Detecting Ashton's SSVEP.ipynb b/lab5/Detecting Ashton's SSVEP.ipynb new file mode 100644 index 0000000..63792a7 --- /dev/null +++ b/lab5/Detecting Ashton's SSVEP.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from scipy import signal\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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8l06Pnj2zj2+8MXndWvnF2h4IHtK1eM9zbcqdIyqnwLzxBtx8c+Fyn38enb9i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\nWYxZTH1VtxlaFKNHw4gRLm2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(d.channel_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# b, a = signal.butter(5, (8.0/125.0, 40.0/125), btype='bandpass')\n", + "\n", + "x = d.channel_0.values\n", + "# sig_f = signal.lfilter(b, a, x)\n", + "# sig = sig_f[500:]\n", + "sig = x" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# freq, y = signal.welch(sig, fs=250.0)\n", + "fft = np.log(np.abs(np.fft.rfft(sig)))\n", + "fftfreq = np.fft.rfftfreq(len(sig), d=1/250.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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573J/BzvsUN7+WbXKKvVOQX1lIqce9APM5YB99jG95Xbe2TSV884240fVXO3d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\n9KtqRR8AtgHwnOv9KQCurPR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.plot(freq, y)\n", + "plt.plot(fftfreq, fft)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fftfreq, fft = signal.welch(sig, fs=250.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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01fTxx+/+2JFHDtz7SN17b3q+zzwzMMcbyjo6oLW18lXU4IAw64thExB77AGL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\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(fftfreq, fft)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "b, a = signal.butter(8, (6.5/125.0, 40.0/125), btype='bandpass')\n", + "sig_f = signal.lfilter(b, a, sig)[1000:]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fftfreq, fft = signal.welch(sig_f, fs=250.0)\n", + "plt.plot(fftfreq, fft)\n", + "plt.xlim(5, 40)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 8000)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1rfe0MbV9BAOAMd7zR4CBSdvHq+oWVa0GFgD9RKQd0ENVn/X2exjI8TPcpNOk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\nRGQucJuqPiMiE4CnVPVf9by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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fftfreq, fft = signal.welch(sig, fs=250.0)\n", + "plt.plot(fftfreq, fft)\n", + "plt.xlim(5, 40)\n", + "plt.ylim(0, 8000)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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rPVgNPu4mOTcgtaOfY7pgQevMGdqYfflcTdAlbJrwRUvDVOfUKXgfX3QrhEpD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\nq/1L3zInwmkhv/YVMYcsWtR5TsvW5PopWi83yLK97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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plt.specgram(sig, Fs=250.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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N6ho62d1RI2oQtsCsFdugzhc5OJkU1bmtUBdQwioT2koeWAO2jJHuEztvLI5Y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\nS1fqGUN9TtYbJ3wngB8mol9Ak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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plt.specgram(sig_f, Fs=250.0, NFFT=256, noverlap=128)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/lab5/LSLHandoff.py b/lab5/LSLHandoff.py new file mode 100644 index 0000000..dcdabc3 --- /dev/null +++ b/lab5/LSLHandoff.py @@ -0,0 +1,19 @@ +## code by Alexandre Barachant +## adapted by Pierre Karashchuk for compatibility with Python3 + +from pylsl import StreamInfo, StreamOutlet +import numpy as np +try: + input = raw_input +except NameError: + pass + +info = StreamInfo('Ganglion_EEG', 'EEG', 4, 200, 'float32', + 'Ganglion_123456789') +outlet = StreamOutlet(info) +while True: + strSample = input().split(': ', 1) + sample = 1e6*np.array(list(map(float, strSample[1].split(' ')))) + stamp = float(strSample[0])*1e-3 + outlet.push_sample(sample, stamp) + # print('Pushed Sample At: ' + strSample[0]) diff --git a/lab5/README.md b/lab5/README.md new file mode 100644 index 0000000..5b2928c --- /dev/null +++ b/lab5/README.md @@ -0,0 +1,37 @@ +# Lab 4: Neurofeedback + +### Introduction +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. + +### Setup + +First, install the libraries: +``` bash +npm install +pip install -r requirements.txt +``` + +(If you don't have `npm`, you can install by running `brew install node`. You can get `brew` from https://brew.sh/) + +### Stimulus Presentation + Recording + + +- Attach Ganglion to participant's head. +- Record positions of EEG according to 10-20 system. +- Have participant sit in chair in front of monitor +- Connect to the ganglion and stream data: `node ganglion-lsl.js` +- Run lsl-viewer to check connections and stream: `python lsl-viewer.py` +- Run neurofeedback: `python neurofeedback.py` + +When running `neurofeedback.py`, it will show a bar representing the ratio of beta (12-20Hz) to theta (4-8Hz) rhythms in all 4 electrodes. +The goal is to increase beta while decreasing theta, which has been shown to improve symptoms of ADHD [1]. + +You can play around with which frequency bands to use in the ratio for the bar by changing the following two variables in `neurofeedback.py`: + +``` python +decrease_fs = [4, 8] +increase_fs = [12, 20] +``` + +References +[1] Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clinical EEG and neuroscience, 40(3), 180-189. [(PDF)](http://www.bakerneuropsychology.com/files/Arns_2009_ClinEEGNeurosci_Efficacy_for_ADHD_meta-analysis.pdf) diff --git a/lab5/ganglion-lsl.js b/lab5/ganglion-lsl.js new file mode 100644 index 0000000..79a76d8 --- /dev/null +++ b/lab5/ganglion-lsl.js @@ -0,0 +1,29 @@ +// code by Alexandre Barachant + +const Ganglion = require('openbci-ganglion').Ganglion; +const ganglion = new Ganglion(); +// Construct LSL Handoff Python Shell +var PythonShell = require('python-shell'); +var lsloutlet = new PythonShell('LSLHandoff.py'); + +lsloutlet.on('message', function(message){ + console.log('LslOutlet: ' + message); +}); +console.log('Python Shell Created for LSLHandoff'); + +ganglion.once('ganglionFound', (peripheral) => { + // Stop searching for BLE devices once a ganglion is found. + ganglion.searchStop(); + ganglion.on('sample', (sample) => { + /** Work with sample */ + st = sample.channelData.join(' '); + var s = ''+ sample.timeStamp + ': '+ st + lsloutlet.send(s) + }); + ganglion.once('ready', () => { + ganglion.streamStart(); + }); + ganglion.connect(peripheral); +}); +// Start scanning for BLE devices +ganglion.searchStart(); diff --git a/lab5/lsl-record.py b/lab5/lsl-record.py new file mode 100755 index 0000000..7fc24b1 --- /dev/null +++ b/lab5/lsl-record.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python +## code by Alexandre Barachant +import numpy as np +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 + +default_fname = ("data/data_%s.csv" % strftime("%Y-%m-%d-%H.%M.%S", gmtime())) +parser = OptionParser() +parser.add_option("-d", "--duration", + dest="duration", type='int', default=300, + 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=2) + +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('type', 'Markers', timeout=2) + +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() +print('Start recording at time t=%.3f' % t_init) +while (time() - t_init) < options.duration: + 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 + +res = np.concatenate(res, axis=0) +timestamps = np.array(timestamps) + +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) + +data['Marker'] = 0 +# process markers: +for marker in markers: + # find index of margers + ix = np.argmin(np.abs(marker[1] - timestamps)) + val = timestamps[ix] + data.loc[ix, 'Marker'] = marker[0][0] + + +data.to_csv(options.filename, float_format='%.3f', index=False) + +print('Done !') diff --git a/lab5/lsl-viewer.py b/lab5/lsl-viewer.py new file mode 100755 index 0000000..cf550d7 --- /dev/null +++ b/lab5/lsl-viewer.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python +## code by Alexandre Barachant +import numpy as np +import matplotlib.pyplot as plt +from scipy.signal import butter, filtfilt +from time import time, sleep +from pylsl import StreamInlet, resolve_byprop +import seaborn as sns +from threading import Thread + +sns.set(style="whitegrid") + +from optparse import OptionParser + +parser = OptionParser() + +parser.add_option("-w", "--window", + dest="window", type='float', default=5., + help="window lenght to display in seconds.") +parser.add_option("-s", "--scale", + dest="scale", type='float', default=100, + help="scale in uV") +parser.add_option("-r", "--refresh", + dest="refresh", type='float', default=0.2, + help="refresh rate in seconds.") +parser.add_option("-f", "--figure", + dest="figure", type='string', default="15x6", + help="window size.") + +filt = True +subsample = 2 +buf = 12 + +(options, args) = parser.parse_args() + +window = options.window +scale = options.scale +figsize = np.int16(options.figure.split('x')) + +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") + + +class LSLViewer(): + + def __init__(self, stream, fig, axes, window, scale, dejitter=True): + """Init""" + self.stream = stream + self.window = window + self.scale = scale + self.dejitter = dejitter + self.inlet = StreamInlet(stream, max_chunklen=buf) + self.filt = True + 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') + + def update_plot(self): + k = 0 + while self.started: + samples, timestamps = self.inlet.pull_chunk(timeout=1.0, + max_samples=12) + 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: + if self.filt: + data_f = filtfilt(self.bf, self.af, self.data, axis=0) + else: + data_f = self.data + data_f -= data_f.mean(axis=0) + + for ii in range(self.n_chan): + self.lines[ii].set_xdata(self.times[::subsample] - + self.times[-1]) + self.lines[ii].set_ydata(data_f[::subsample, ii] / + self.scale - ii) + + impedances = np.std(data_f, axis=0) + ticks_labels = ['%s - %.2f' % + (self.ch_names[ii], impedances[ii]) + for ii in range(self.n_chan)] + self.axes.set_yticklabels(ticks_labels) + self.axes.set_xlim(-self.window, 0) + self.fig.canvas.draw() + k = 0 + else: + sleep(0.2) + + def onclick(self, event): + print((event.button, event.x, event.y, event.xdata, event.ydata)) + + def OnKeypress(self, event): + if event.key == '/': + self.scale *= 1.2 + elif event.key == '*': + self.scale /= 1.2 + 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_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 = """ + toggle filter : d + toogle full screen : f + zoom out : / + zoom in : * + increase time scale : - + decrease time scale : + + """ +print(help_str) +lslv.start() + +plt.show() +lslv.stop() diff --git a/lab5/package.json b/lab5/package.json new file mode 100644 index 0000000..3c761ad --- /dev/null +++ b/lab5/package.json @@ -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" +} diff --git a/lab5/requirements.txt b/lab5/requirements.txt new file mode 100644 index 0000000..6ac74d8 --- /dev/null +++ b/lab5/requirements.txt @@ -0,0 +1,6 @@ +pygame +numpy +scipy +matplotlib +pandas +seaborn diff --git a/lab5/stimulus.py b/lab5/stimulus.py new file mode 100755 index 0000000..7aab8b8 --- /dev/null +++ b/lab5/stimulus.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python2 + +import pygame +import platform +import time +from pygame.locals import * + +fullscreen_on = False + +size = 1280,800 +width, height = size + +white = 255,255,255 +black = 0,0,0 +red = 255,0,0 +green = 0,255,0 +blue = 0,0,255 +chartreuse = 127,255,0 +light_green = 131,255,100 +nice_red = 207,45,64 +sky_blue = 100,244,255 +nice_blue = 0,194,255 +dark_gray = 20,20,20 + + +space_x = 100 +space_y = 100 + +rect_width = (width - space_x*3)/2.0 +rect_height = (height - space_y *3)/2.0 +rect_xs = [space_x, space_x*2+rect_width, space_x, space_x*2+rect_width] +rect_ys = [space_y, space_y, space_y*2+rect_height, space_y*2+rect_height] + +freqs = [10, 14, 17, 20] +stimuli = [0, 0, 0, 0] +wait_times = [0,0,0,0] + + + +if fullscreen_on: + screen = pygame.display.set_mode(size, FULLSCREEN) +else: + screen = pygame.display.set_mode(size) + +if platform.system() == 'Windows': + wallclock = time.clock +else: + wallclock = time.time + +pygame.mouse.set_visible(False) + + +def check_for_escape(): + event = pygame.event.poll() + if event.type == 0: + return + elif event.dict.get('key', -1) == K_ESCAPE: + pygame.quit() + exit() + +def draw_stimuli(): + screen.fill(black) + + for s, x, y in zip(stimuli, rect_xs, rect_ys): + if s == 0: + col = black + else: + col = white + + rect = ((x, y), (rect_width, rect_height)) + screen.fill(col, rect) + + pygame.display.flip() + + +to_wait = 0 + +while True: + t = time.time() + check_for_escape() + + draw_stimuli() + + for i in range(len(stimuli)): + wait = wait_times[i] + if wait <= 1e-5: + stimuli[i] = 1 - stimuli[i] + wait_times[i] = 1.0/freqs[i] + + to_wait = 0.005 + time.sleep(to_wait) + for i in range(len(stimuli)): + wait_times[i] -= time.time() - t +