{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cross-subject classification\n", "\n", "This is an example of how one might pool data from multiple subjects in order to look for ERPs and run classification pipelines.\n", "\n", "The `utils.load_data` function will load multiple subjects data for the same experiment into the same Raw data structure by simply providing it's `subject_nb` argument with an array of subject numbers" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import sys\n", "from collections import OrderedDict\n", "\n", "from mne import create_info, concatenate_raws, concatenate_epochs\n", "from mne.io import RawArray\n", "from mne.channels import read_montage\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from glob import glob\n", "import seaborn as sns\n", "from matplotlib import pyplot as plt\n", "from mne import Epochs, find_events\n", "\n", "sys.path.append(os.path.join(os.path.expanduser(\"~\"), \"eeg-notebooks\", 'utils'))\n", "import utils\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read data and convert them in MNE objects\n", "\n", "Data is saved in csv file for more convenience. Then we will convert them into MNE data object so we can pre-process and epoch them" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30720\n", " Range : 0 ... 30719 = 0.000 ... 119.996 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30744\n", " Range : 0 ... 30743 = 0.000 ... 120.090 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30720\n", " Range : 0 ... 30719 = 0.000 ... 119.996 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Setting up band-pass filter from 1 - 30 Hz\n", "3089 events found\n", "Events id: [1 2]\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30720\n", " Range : 0 ... 30719 = 0.000 ... 119.996 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30744\n", " Range : 0 ... 30743 = 0.000 ... 120.090 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Creating RawArray with float64 data, n_channels=5, n_times=30732\n", " Range : 0 ... 30731 = 0.000 ... 120.043 secs\n", "Ready.\n", "Setting up band-pass filter from 1 - 30 Hz\n", "1548 events found\n", "Events id: [1 2]\n" ] } ], "source": [ "all_epochs = []\n", "subjects = [1, 2]\n", "\n", "for subject in subjects:\n", " raw = utils.load_data('visual/P300', sfreq=256., \n", " subject_nb=subject, session_nb='all', \n", " ch_ind=[0, 1, 2, 3])\n", " \n", " raw.filter(1, 30, method='iir')\n", "\n", " events = find_events(raw)\n", " event_id = {'Non-Target': 1, 'Target': 2}\n", "\n", " epochs = Epochs(raw, events=events, event_id=event_id, tmin=-0.1, \n", " tmax=0.8, baseline=None, reject={'eeg': 100e-6}, \n", " preload=True, verbose=False, picks=[0,1,2,3], add_eeg_ref=False)\n", " all_epochs.append(epochs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Epoch average\n", "\n", "Now we can plot the average ERP for both conditions, and see if there is something" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4557 matching events found\n" ] }, { "data": { "image/png": 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BQmVFVB5ZxeT3TqO2vJb3lr+HKUWTdpWxc0fKIJ8j2LYDtXQl9jX/esiTpiGg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qlzW5IeHk1cRH2iEhthMkVhlEMwyIYK53uydOkrblV9ijcDLwIvnd5gs9kigfeA9ZfUgxpCCMHx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\ntRMjzYNAiyPjmtGcfc52uQbaNOvPrsPrUGx2FIejT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aCqBIVhDn2MEo7JEtbJ7ai6oEmh\niSZ7IrgrNIpSBFs6qgQdxvZND2iMmxPGU3VSsNQ4E71nR2TrZkalTUrElp0oC1fhKQlz47QYM29N\n5pCjkqX+FRwI7ecS+2BSYy5kNIxY/QPK0u/RFNDNCkqrZihulxHUH8xHhCMsGmoiahWYNGjX8RKc\n9v9jKJwODsXO3XfdxbCuQ2l3V2d2JO+sCZoUu53EkaPRyssIbNpAYNNG9HAY78jLjHFNCORJaolC\nUVE9HtQE7ykrxKHSUh754kv+PKQny7KMd7yJpRFO1Vnv2JZiSiYmY4T69kB+vRQRjiB/2Iro1a3m\nO2ZhItmcfEa6MoAiFNLMDciPFtRSsdT8FbBtF6aP5yGiMaTNinb1KGjUEFNCAoq9bqJKj8cgGkGP\nxcnWbTSU2QhFRbHbz2i1kGnJ4NqUCVRpAQ5GDxHSQ1iXbqT5t/uwKuWIJ6xwmuFa6hrxfQdQZy1A\n6DoywY0+egiyaUNE7n6UecsQgRDqzK+I32jF3CanFu1WFQou1VXLMuDn0IIBKpcbKoOOTl2wtWhV\n7/eOw5yUTKbNTsDspfSKVCKDig013T0HjSRoOAIWM6QlozdvhKVrFzzuBjjMLmSDZCoJENRPGIF3\n6NaRdm3aEV8QQr3MxUr/GvxaFaO9l9SwRbYEt7Kmai0A/d19oELywmPPM2/BvP90wPSrcLYr3M9z\nc3PraHDm5OQk5OTkbM7NzT074upvCNmqKXp6ChmkYMGQiURVa/F6yy1hqiwawmpFMVuwACPJZrgc\niV/zE5FRklQvqi4gFiNKjO9CP7Ai+B1BwsyVq1kpttLe1AoHVg7Ej5KnHwDAjYPx9hHYHCkIm1G6\ntpxGSUt1uVCcTtIqXNBL4O/aDrFjjyEpnF8EgWANnQF3de+U141M9kJy9cLI6z7BQRXC4LnbHbXM\nKWvdo3gcPViFp2VHPM16Eti4nqp13xEvK0Wv8qNX+Wu2an0EWjYzk9dcZZFlCzn/3I6pexdklzZG\nBvZwAesr1wMKKT6FzE4DcCr/cznh/w0IIbjiyrFsXLQJd46XPZG9aFKrIzYgVBXXBb2pWLyQqnVr\nSb160ikzgZFIhL///e/MmTOnzt+Oqy1q+QWstuUCgtQqC21bGcGV15RQ46nzc7jNbpTLJlKwZRci\nFMI0bznu6xqRnJKNUp2l1HzlxPftJfb+DERlddWlYw6ySRZi32GUbXkoB47inP4Vtt9fhblx+zrV\nKr9WhS/uoypYgmfGIpJ3l2N2J8D9Y1HPEDApwujB+fnSsj4D1lPuw+5AbdoEbcIlqDO/QlT4UT+Y\ng7h6DNZkFYUIOhKJTnzbPvQ5C0HTEDY7KROuwda8JWqCF8XuIBCvoqqqhODQQchABLZsR125AXfT\nzqi9ssF2hsq3lIQP7afsK0Ox0tKoMd6LL0E5B9dzVaikWRqQmOQlkhBGCwVQQlFUXcFisaO63HX2\nF5dx/FoVlZq/xtRUCMFDzzzM4/f9hRv7TuZvr/2de4bcUe8xX3v2n8z5eA5rVq3+H90Hci64srwL\naU1BiqNwrNgYB32V4Ks855SkBLBbwWbDZLdyzWYHH7or2J8cYkU/Ez/2ctNIb0DAHOegOEa8ulJk\nwUx30ZrOohUpJOAnxHa5j3VyOxVUcTTLeJf2NzHGD2eVpMdmje6b43irW9pCHisHm5o5kBFnf4ZO\nfqZAU2BvRoy9GQpw4n00RyWDVsfpv9YQuZDJCcjWzdA75hg9uMehqAY9sUtbtEaZqDO/wuGr5PpX\niph3YzabUovZFz/ENP/7ZMtUUg4FCCX7Kb3VQkmSglRAJZ8mZNJGNKOl7M/+yjw2eoxEwUBzD7wN\nmtTIof8f6ocqVJo3bE6bke3ZM3MXeQ/tJapHa2ht5qRkksdPBCCwaQOhHduI7N+HvW07zGmGyiOA\nMJuxZGZhisfQAwFMqQ1QLHWrqtOfeoKGLifpfQ1TcQVBtqUhrlPM84pQyDCnU5AMwRaNIe8ActV6\n6NUNIQSJagIJasI50XhVoRqBU6wATdfQfGXIH3eifrYQoWlIl8Por2mYhSkx0UgUY1RkLMJMXMYJ\n6kEwmcFk5tcszV2qs6aHT++egVz4KkILo3+zEjF2ZP1rLiTxwkLUj+ciQmGjovS70ZCaYlT/2rRE\nS/SgTv8CEQyhzpqPdlcKltQ0HIoDl+LErtjPeM8qVyxFq/ABgsRRl51VP5vqcOBxNMfhT6VUdVIx\nMBFtYA+QEgsWXMKBiooJBauwoLo9mBKTEIqCAxe+eAXl8fKa+fj2B+/k4Tv+xA3jbmV7dCebgz+y\nP3KALo5OhPQw6wMbkBgWHgPd/Xjuj88y8vKR9Ora6xx/if8uThs05eTk9AR6Ax1zcnLuom5Jqzlw\n/nk0vwCmxCQSPBnYYw6Dxy1ODBCKzY7Jk0CG2UxhrIiAVpuSpwoV78l9OApgMmMDhtiH0NXTlfkV\n37AttIMKvZI1kdpGYNmWLK5JvgpPteqUQ3Wc1ovlOIQQmLxeMpzt0Ut3E+hkQnY6dUn1Z1sDEhQV\n1eGoVwnv5zCbraSmZGGTFuK+clwX9MHZsxexo0eIHstH8xnKXAElSkzRGJ7vY2/TQ1QkCFa3CzN0\n0SrkN6vB66ZCq2TbncZirLfnAlSLFedZUJH+/4aJV01g1u9m0eju5hyIHCSkh+tt9EwYchEVixei\nB6qo2rgOd6++9e7vvffeo1OnTnTv3r3O3/x6FbFQgKKVi9g50Hj+B3j6oSoqVsVConr6TK4jLRvv\nsOH45s5Gbs9FrFxPrLcFxWpFD4WIFhVS8v7baP5KhMlMwpgrqGybSZQ4sktbZKsmBh2n1If6wWzi\nN16FKbN2j49bdeGMm9C+2Yi6w+i/0cZcyP9r777jpKrOBo7/7r1zp+1sL+wuSy+XpoggTWxIFcWK\nFaNGY0GNRn3VRGOJLcbEqLHE5M1rFFEUe6EoiIAlFmIXrhQB6Szby+yUe98/7jLusoUBZhs838+H\nz+7MnLlz5jI7555znvMcV2Zz6f9/3gdGQSFshwnaNSgoeFVPbNQzZIUotyqoiFY0uweNFkjG7m9g\nnXUC2vNvoxSVovzjeXzHj8fXf5CzueyHywjVzv5o6RlknXcB3u49cWVkxhqkZD2dZF86VmqImqkZ\nbNv0CJEd2yl65QX03Fx8vfo0O2MULS6i+NWXncEKl07mtHNxpe/blndu1e1cPOkpsIfJOpfiIt2V\nRqqWwtbwdoKWEz6lqip3PnQXi+e9x61X3UJmdiZnX3wOx00eS2Z2Jj+t+4nH73+ULz75grcXv03n\nvM7Nv1AHklXQD2tYbeKHqmq8m4px7axAqwyihGvXBkWjKLruzOLrOqrPj+r3Oz99PkJ+F2GfRpUX\nIsrPazFcwLl2hHfsT/jcXkmJq5oSfg6R9qAzwjWY0d6hJLl2fTcoZJDGmEg2I8OHsSr8I2vsjbwA\nDFcG0IUc+tekoLs3omSsx67YgmJZ+Mpq6PdVDf1q90Su9MOqQwL80N/DtkybCk+UVDWZ7koeI9z9\nST3KgzUq4mSJrTtj6HLhSgo4me4UFRUVKxikRnMRvfgMtOfexL1lB6c9+RO9xuWxcGg1xXqQn5Qd\n/NQNnHm2n0WxWMNG1tgbnTtqP6P5ZHF0+jF4dIlSiIdf9XPaVWdw75Q7WXPVWoI5QZzhX4eek0vG\naWeid8qldOECrOoqKpd/1uixtPQM0sZPwjdgEHpWTizLGkBNWRkPPPU0d484nPWHZQGl5Ot5eDVP\ns1lAd3WcNo0ZQ82qdShr1uP5eg25RxzbYLDQtm3sUA12JOJkitT1RgcLdUUnK+hjU8la+OJb1NcX\nOQkJ0lKInn8yakFntNRU3IpOipZCQEuq91qWbVESLY0lq0gEJS8Xa+ghKJ9+hfLB51jHjETLapg4\nxiotQXlpXix7qe+MU1G79yMUcDuDc7ZF1OfDOusE1GdeQ9lWiDb3A/LO+xWepPjWN1k1NZQuesc5\n/oCB+PYwy7Q7V3IKOYEBZFaUU122Ey0UjUVTKS4dLZCMFgg0+L9JczkbtBeGneUlI44aQXpmBknv\neZg0ZRzvlr5HSbSUxeVLY8/JcWVzUdZ0tq7fyqvPvcLyr5fvVV3bgz19S/mA43EmH69r5PFq4JZE\nV2pfaG4P2Rnd9vjFm+3KImxvIWQ1ko2rCWmuNM7NPJNt4e18VrmcjaFNBK2a2GLpQ/wDY6+rKApZ\njexh0RxV1+ncqT/bqjZTXr4dK1hTf68lVXNGUDQVn+pzFgC6nLCRoNum3KqMXfg0xat66KTnxL5M\n9MwsXKlpTux5QVfcBV1iZf12hE3sIBObEdZ/+Nj+lmVH6hyywiJnhwXFZXx8vAtLVfBbbg7POwqv\n6pFGrxEjjhhBuCxE+foylG5QFC5qtNPkzsvH06sPNWtWUbbkvUY7TbZt8+ijj/Lwww83+lhJuJjI\n9ytZlr4e0EircTO452gUnM/9nkarFEUhfdIUgiu/J7jKpGTemyguF/5DDyNorqDojVewg0EU3U3W\neRfg69uP1NRUdrjKqQpXYA33Y6GgvvIOyubtKLNeI/KLU3Fl56BozmfDClYRMdegLfnUuT3sELTD\nDml0lA6cBa9Zrsx6CSHcirvehULsftVNpppBupZWO5NS1uTmpJ60TFIOHYXiyqHopdlYlZWUzHsr\nlnkodsyCrmSecz7ebj3qXVTUpbrdeAq6knXWeWz9+9+IlpVS9OocsqZfhCe/M0oj4WvRsjLKP/mY\nqm++BCBl3ER8Rv9WTaagKiq5eg7bwtuprvP9cdzksRw94RgWvrWQN2a/xp3X3UE4FMYf8HPOxefy\n4qI59M+Nd3CnY9Azs9GSU7BqakhRkknvu/f7ue36hKbZFoVKKVXUxBLseHUXU7UpDGckX4dWUmKV\n4VJc9PL0oJ+vb7OZJl3AIHIZGIlwPTdxYtI4Z4+1nCh2Tg726MOd5BLbi5zQ9FDYCalLDeDplMWg\n5CQG7X7QXQMZmk0sqYWionq9TifQ48WreknTUvCqXlRFxfbYFPmL2OleT/SCU1HnzEdds4HBC7cw\n6D1YYaj8lK9SlKHiT8smNbc7ua5O+ALplFHF98GV/BBcHdu/qaurM2dknILuDuwxHb9wJGl+hvc/\ngtS+6Xy16EuKuhXHBmsBFFXFnd+ZlGOOw2v0p+rL5QTXriZa/nPopVVVhV0TJFpcxM4Xn8PXfyDp\np05Dz8xCSwpgR6PMvPdustw6wztl82QPZ+Cnm6crftW/x+8oVVHpPGYyGxa8R2TzZqKvz8fqMwQ1\nLR1FUbBCIWffn/JybKt+ogdF05w1SbUX6HY0il1TgxYO4X/nI2o+ddoNOyeD6PSTcXUpQPUlxVJu\nN5a1TVVUMlzpeFUvhY0kl/i5nIJX8aIoCiErXG+fsd0pKKgTj8Fe/g1KdRBr/mKs06fg8afiUd2o\nqARLCwm/8zHR71YBznd85qhxzqCdbcc2vo2m++EQP+HRWwl/9B+sjz6jZsBQ3KOPimvGqPyjZUQK\nncQS6VNO3mP5Rt+PouBKTiE5OcU555Gw833QyAxkXSlaMpZtURQpRlEUbrr7Jq698FoWTHmHvp36\n8Fnlcn4IrsajeDC8fTgyMBK/5ueBW+/nl1deTI/8Nssht8+avco1TXMJsMQwjAWmaU5spTrtk1Qt\nNa6L9l1xsptCm7H2Mt16Jz2HE9MmN1smPc56NFavXH9nPJ4kSiOlWLaFZqt4XF48qge3ouNVvQ2O\nrQPJJFNtVVMYLmr0Dz2gJTV60ay4XOjZOWipzpeYVRMEGzyKQhoWpdFyjgkP4+vIGiq1ap65NINf\nrR1MYdU2Ph24FrAY4T0cj+6X0LwmqKrKhMkTMN9bTfJFA1lZ8wNdvV0aLZsy5lh2rFlF9crvCe8s\nRM+sP3L1wQcfEIlEOO64hmk5K61Kgjt3UPb+Qr45w/miHZM2BpfiIklL2uOmpbtoSQGyp1/Alkce\nJLKzkOLXX6b4jVdrM4Y5sfOZZ/+CpMFDcKU5M1d5dhI71EIqXDrR0UdgVQfR5i1FXfsT1uy3CJ8+\nEcXnc2Lqi0udUIXa2G5OGovqbhiOpgCprlTStaZj7puiKiqprhRSXSkErSARO+psfYMSS1frU30o\n2RDu50W/7GpK351P9YrvYqnNVX8SKcceT2DYCNz5nZ1ELs29ptuNf9Bg0iadSMlbrxE0V1D+wRKU\no45Fz+kUa3x2hTkG162l6HVnGzp31+6kTZi8xwaqJTgdp04UR0vqjcRqmsbEkycy8eSJzqa7oRCq\nqqLreiyD5oFE0TS0QDKZaQWkq6lOpjm79qItHHayNtq2czG36/O4ay8lXf/54qY2jXdXRYktjK+r\nMwE6J3Xb+/rhdL4AXGnOoJwVDmEHq53MeIoKBbnYBbm7PXHX+tbai1GvF8XlqjdIsWuty64ZYV3R\nydIzGnRkFEUh052JN9vLZtcaoueeiP21ifrhf9EKixm4RmOAVYDVbzRkpaP6A2ipPydYGZI0mLAd\nZmNoE+laWr3ojngyTArn/6anpzvdTu7J+tfX8sO5q+nurf95UlQVvVMeqtePnp5OyrHH13vctizC\nWzZTunABNT+uoXrFd0SKi5yZ7sxMojU1PPL0M1w5qB/hPl3ZrDozCd08XZsMzdudqmlkTj2dbX//\nG6FNP1Hy9hskjzkGRVVjHSU7EiFaWYFVXY3q9aHVhvra0Si7Fk5bwSDV5grK3l9EtNhZD2t3yyd6\n1om4OuejeX1kuTIbzUq7O7/qo8DdmZJoKeXR8lg0gq7opLpSCKhJ9b7XQlaIwkhRkwPSSuc8rJFD\nUD5cjvrhf0ntdzipvdNRNBs7HET7di07FzmZLn2DDiV9yimxRDyKohDQkn4eQM3OpWaKn62r1xHe\nvpWi117C26Mn7s6NXyvsYoXDlLw7DwBP7z74Bx6yx/OwJ4qmNTrQ15Q0VyohO0RFtJIjxgxn1LGj\neOSeh/ndH2/hpLQTGpRf8s4Svv3iW57697/3u65tocmre8Mwkk3T3DU8Mc0wjCaDPkzTLGvqsdYS\nzx/NLrqi00nPYWto2z7ultE4t6qTqjXcBydeiqKQ4UonoCZhYTebWWZ3PtVHZ3ceOyNFVFqVWLaN\nS9FIc6XWG4lqjOp2o7rrz45l2znUhDah2xbnVU7j/4pnUaJU8Eivz4gSJYJFCgFGZx4FIKF5zTh5\n6snc+Oeb6XuRsyGslWI1etEZGD2GwheexQ5WU7r4XbLOOKfe44899hgzZsxotBOxs2wL9sJlfNij\nBEt1kRR1c0TGKBTY6xTw7s5dyb7wVxS//TrBld/HOkzugq5knHEOvj59nL0faimKQo6ejVtxUxyA\nyNgjsaqqUZd8hrpyLcpTLxMdfyRKNIq64AOUkjJsTSN6+kT07IbRvV7VQ6YrI65d0Pek2T2YFJxY\nf9sm4/QzsWpqCG/bihYIOOuWXDp6bu4eFwrvonq9pI6bSM3a1VR//y2l78zFlZmJPxpB9Seh6G6i\nFeVEqyrZ+cJz2MFqZ63UWeftc1heIuz63vEobrZHCtk9o6qiKHhq10U533Etl6yjLdVd86fE+X/e\nnGxXFjZ2g3DwRFF1N+hudvU3bNuCqFXbwQM01enQ7WFVlqKosbbCrbgb3V+mriQtiYKMPmzW1hE+\nTCN6WH8nK1vsQktxFuT7G7bJuqLTw9O93n2eXaGlIi7prjSGThnGv+79jG+2f8fxqQ1D33aF/msp\nKVjBoDMYZNtOh9+2cSWn4O5cQPlHyyh9Zz7hrVvY9uTfCIwYzdJly6ioquK4gnx+nGhg8QEA3T3d\n9mpGMDBsBKV9FxL8YQWlC+cT2rYFd+cCIjt3Etq0kfDWLbV7ltWpt8eD6k9C9XqxQyEixUVO1kfn\nUQJHHkXyuPFE87LQdDeevYxw2TXrlKalErLDqChNfvbcqpt8dy4V0UqKIyUNBqQVFNxTT8BauQ5r\n504qX30V3/npuNLSCK5eRdHLswEbPTePrHN+gZ7e+Hri2Ovl5pF+6hls/8djRAp3ULxgLpmnn4Ur\nten2u+LTjwlvdjJgpk9umU2h45HlyiRkhwhZYW6+52ZOGD6ZYyYey5HH1Y+Yqa6q5vZrfs+9f7uP\nnOTmMw62V8192rbh7MUETmrxxvoXtQtr6HDpk3yqjxw9m+3hHQnpOGm1M1iJCK/Z1wZEVVSy9Syy\nySJiR/YrXE5TNNJdaRSGi+ie1JMzldOZXfRSbOPdFCXAJdkXkKQlSWjeHkweP5kLf3ERofIQ69wb\nCFrBRlNRax4PgaFHUP7hUio++oCMU89Erb0Q2b59OwsWLODJJ59s8LyKYAnV5vdUf/4py2c4o9Gj\nU0ejKzoBLRBXFre6FJcLXx8D7ezzCW36iUhJMXpOJ/TsTs7MZBOL/9NcqfhVH2VpyVROmoCleogu\n/gBl83ZcT78aK2crCta0SbgGD4qtw3MpGgEtQJLqT0hnKV6KqqLn5RMp2omilKN2dUZtVZ+/QVrV\neOhp6WSePZ1tjz1EeNtWiubMhlPPwDfACZCKVlRQ+PwzhLduBhQyTz8Lbx+jXWzInKQl0VnRqbAq\nqbKqGoQwuxSNXL1Tu6hrovk1f5NJUvaVUpt2fgeFDWacWoKiqOBS9/oz21xoU1N8qo+81O5scWlE\nSkuwa9sFxe1FS0lxOnRxSpZZpr3iU330ze5LpyPzWfjqu1x+w8VNrqFWVBXN3/iAppaaRuqx49Bz\nOlH06hysykrKl73Pk+8t48IBfUmaMJ4NXV1Q7lwUZ7sy9+pvX3G5yLnoV2z/vycJrjKp/uYrqr/5\nqtnn2DU1RGtqqNeVUhS8fQxSxo7H27U7ek6n/d5/SlVUvEp87cyuGaEqq5qqaBVRomhoeFUvSTnd\nqJh2LtuefJTIzkK2P/k39PwCataudrak8CeRPf0i3Hn5e3wdRdMIHHY4VSOPpOLjDyj/YAn+gYeS\nNHhIo5EOVnU1pbWzTO4uXfEf1nC/tNayK2JhS2grWZ2y+duzj3LVeVfxzFvP0P/QAYCTxOraC37N\nsNHDmHbCGW1W1/3V3CevbjjeWPZ1C/N2LElLIl9xsSNSSMgKo+CMTOuKjo1NlVUdyy7VHLV2pH1v\nL05bUiI6MSlaCuXRCmqsEIP8A7jOfTVrgmspjOxkROCI2EXG3uxgfTBKDaQyaNQgti7dhG+Kj7Jo\neZP796QcN47yD5cSKdpJ1Zf/JTDUSVr5/PPPM3XqVFJT689k2pbFzp9Wor04l4+HqUR0BQ9uRqU4\nGWn2dVZA9Xpxd3LCyuxwGNXnxZWeucdU2G7VTZaaSWZuBpHJ2VR06kHpu/OI7HQWwiqd89CPHYNv\nyBB8gRx0xYWK2qYjzYqioGdmYWdkOiPmqhpXLHlTPJ27kHX+RWx/8jGi5WXsfPE5PD17oyUnU22u\nwA464R5pk6cQOGJEm4TlNcWtuslQ3WSQTo1VQ9CqIUIEF64DekuBDC2xHaZdds3Cxjvj5FO9JGl+\nXIqOAlRGq6iwKpoMJdcUjeTagZGwHaYyWtXsWozdNZdRc0/8qo88fwHb3B4npXNtuOLe0BQ17pAv\n4fCpXrp7utLtpB58+dxyyq+rjCvx1O5Utxs9N48kl4aem0fponf58ssv+bqohKf+eB/a+FFsCM4G\noJu76z6tO9M75ZJ17gWULXufmrWriRQX4UpPR88vwN25AODsI1IAACAASURBVD0rG9XnwwoGiVZU\nOMsEqiqxampQ3B5cKSl4uvdE9fvrZW9rC37V12iq9KQhQ8k8+3yKXnkRq7qamjXOG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ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "conditions = OrderedDict()\n", "conditions['Non-target'] = [1]\n", "conditions['Target'] = [2]\n", "\n", "epochs = concatenate_epochs(all_epochs)\n", "\n", "fig, ax = utils.plot_conditions(epochs, conditions=conditions, \n", " ci=97.5, n_boot=1000, title='',\n", " diff_waveform=(1, 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's a pretty nice P300 visible here, along with some VEP components around 100 and 200ms" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.pipeline import make_pipeline\n", "\n", "from mne.decoding import Vectorizer\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n", "\n", "from sklearn.model_selection import cross_val_score, LeaveOneGroupOut\n", "\n", "from pyriemann.estimation import ERPCovariances\n", "from pyriemann.tangentspace import TangentSpace\n", "from pyriemann.classification import MDM\n", "from pyriemann.spatialfilters import Xdawn\n", "\n", "from collections import OrderedDict\n", "\n", "clfs = OrderedDict()\n", "\n", "clfs['Vect + LR'] = make_pipeline(Vectorizer(), StandardScaler(), LogisticRegression())\n", "clfs['Vect + RegLDA'] = make_pipeline(Vectorizer(), LDA(shrinkage='auto', solver='eigen'))\n", "clfs['Xdawn + RegLDA'] = make_pipeline(Xdawn(4, classes=[1]), Vectorizer(), LDA(shrinkage='auto', solver='eigen'))\n", "clfs['ERPCov + TS'] = make_pipeline(ERPCovariances(), TangentSpace(), LogisticRegression())\n", "clfs['ERPCov + MDM'] = make_pipeline(ERPCovariances(), MDM())\n", "\n", "X = [epochs.copy().pick_types(eeg=True).get_data() for epochs in all_epochs]\n", "y = [epochs.events[:, -1] for epochs in all_epochs]\n", "subjects = [[ii]*len(x) for ii, x in enumerate(X)]\n", "\n", "X = np.concatenate(X)\n", "y = np.concatenate(y)\n", "subjects = np.concatenate(subjects)\n", "\n", "\n", "# define cross validation \n", "cv = LeaveOneGroupOut()\n", "\n", "# run cross validation for each pipeline\n", "auc = []\n", "methods = []\n", "for m in clfs:\n", " res = cross_val_score(clfs[m], X, y==2, groups=subjects, scoring='roc_auc', cv=cv, n_jobs=-1)\n", " auc.extend(res)\n", " methods.extend([m]*len(res))\n", " \n", "results = pd.DataFrame(data=auc, columns=['AUC'])\n", "results['Method'] = methods\n", "\n", "plt.figure(figsize=[8,4])\n", "sns.barplot(data=results, x='AUC', y='Method')\n", "plt.xlim(0.5, 0.85)\n", "sns.despine()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, you'll notice the classification accuracy is decreased significantly compared to single subject analysis with the same data" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }