{ "metadata": { "name": "", "signature": "sha256:de8103a330c1f428bee33f13a2c23aef571360a6af8099fb1d3753d24d21ced7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Explore Data from ANT EEG System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EEG Hacker, 2015-03-17, MIT License" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Purpose" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open data file. Look at behavior of the EEG signals to see what the EEG bias might be doing." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Prepare" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we have to import some libraries...though this might be done automagically for IPython Notebooks...I'm not sure." ] }, { "cell_type": "code", "collapsed": true, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import matplotlib.mlab as mlab" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Load the Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Define Data to Load...be sure to UNZIP the data!!!\n", "pname = 'SavedData/'\n", "test_id = 0;\n", "\n", "if (1): \n", " #day 2...after caffeine\n", " fname = 'OpenBCI-RAW-2015-01-25_18-28-58_eyesOpen_AMSine.txt'\n", " sname = 'OpenBCI, Auditory Stimulation (2015-01-25)'\n", " t_lim_sec = [110.0, 280.0]\n", " test_id = 1\n", "elif (0):\n", " #day 2...after caffeine\n", " fname = 'OpenBCI-RAW-2015-01-25_19-04-27_twoTone_focus2500_focus1000.txt' #2500 is 43 Hz, 1000 is 37 Hz\n", " t_lim_sec = [42.0, 92.0] \n", " test_id = 2\n", "else:\n", " #day 2...after caffeine\n", " fname = 'OpenBCI-RAW-2015-01-25_19-15-48_twoTone_eyesClosed_focus1000_focus2500.txt'\n", " t_lim_sec = [85.0, 135.0]\n", " test_id = 3\n", " \n", "\n", "# load data into numpy array\n", "fs_Hz = 250.0 # assumed sample rate for the EEG data\n", "data = np.loadtxt(pname + fname,\n", " delimiter=',',\n", " skiprows=5)\n", "\n", "#unpack the data...ignore other columns\n", "nchan = 8\n", "eeg_data_uV = data[:, 1:(nchan+1)] # EEG data, microvolts\n", "\n", "# create a vector with the time of each sample\n", "t_sec = np.arange(len(eeg_data_uV[:, 0])) / fs_Hz" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Plot the Raw EEG Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# plot the raw EEG data to see the DC drive\n", "dec_fac = 10; #reduce the number of data points plotted...speeds it up\n", "\n", "plt.figure(figsize=(8,4.5))\n", "plt.plot(t_sec[1::dec_fac],eeg_data_uV[1::dec_fac,:]/1000)\n", "plt.xlabel(\"Time (sec)\") \n", "plt.ylabel(\"Raw EEG (milliVolt)\")\n", "plt.title(sname)\n", "plt.ylim([-60, 60])\n", "plt.xlim([t_sec[0], t_sec[-1]])\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "(0.0, 289.76799999999997)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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URVGUtlBlrnRINAqnnQZz56oyVxRF2VlRZa50SCQCPh8cdpgqc0VRlJ0VbTNXOsTtltp5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\n2fEIQSMg7aR44jx282N41Rm+dJjWMy3a023anTb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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Check to See if the Common Mode Was Canceled" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#common mode is the mean of all signals at a given instance in time\n", "common_mode_uV = np.mean(eeg_data_uV,1) #get mean of each row, not column\n", "rms_common_mode_uV = np.sqrt(np.mean(common_mode_uV**2));\n", "\n", "#plot the common mode\n", "plt.figure(figsize=(8,4.5))\n", "plt.plot(t_sec[1::dec_fac],common_mode_uV[1::dec_fac])\n", "plt.xlabel(\"Time (sec)\") \n", "plt.ylabel(\"Mean Across all EEG Channels (microVolt)\")\n", "plt.title(\"Common Mode Across \" + str(nchan) + \" EEG Channels\\n\" + sname)\n", "plt.ylim(30000*np.array([-1, 1]))\n", "plt.xlim([t_sec[0], t_sec[-1]])\n", "\n", "xl=plt.xlim()\n", "yl=plt.ylim()\n", "plt.text(xl[1]-0.05*np.diff(xl), yl[1]-0.05*np.diff(yl),\n", " \"RMS = \" + str(round(rms_common_mode_uV,3)) + \" uV\",\n", " horizontalalignment='right',verticalalignment='top')\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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RAZqZmVnPqVqVIOls4Cjgr8AU4HlSCcMwYB9gP+DXEfFfPRNq47gqwczM+pNa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\n7LNhxIh07X32gUHdNVOLWT/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