Compute MNE-dSPM inverse solution on single epochs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute dSPM inverse solution on single trial epochs restricted to a brain label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mne.minimum_norm import apply_inverse_epochs,read_inverse_operator\n",
    "from mne.minimum_norm import apply_inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import mne\n",
    "from mne.datasets import sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = sample.data_path()\n",
    "fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\n",
    "fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\n",
    "fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\n",
    "label_name = 'Aud-lh'\n",
    "fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_id, tmin, tmax = 1, -0.2, 0.5\n",
    "\n",
    "# Using the same inverse operator when inspecting single trials Vs. evoked\n",
    "snr = 3.0  # Standard assumption for average data but using it for single trial\n",
    "lambda2 = 1.0 / snr ** 2\n",
    "\n",
    "method = \"dSPM\"  # use dSPM method (could also be MNE or sLORETA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading inverse operator decomposition from /home/reallocing/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...\n",
      "    Reading inverse operator info...\n",
      "    [done]\n",
      "    Reading inverse operator decomposition...\n",
      "    [done]\n",
      "    305 x 305 full covariance (kind = 1) found.\n",
      "    Read a total of 4 projection items:\n",
      "        PCA-v1 (1 x 102) active\n",
      "        PCA-v2 (1 x 102) active\n",
      "        PCA-v3 (1 x 102) active\n",
      "        Average EEG reference (1 x 60) active\n",
      "    Noise covariance matrix read.\n",
      "    22494 x 22494 diagonal covariance (kind = 2) found.\n",
      "    Source covariance matrix read.\n",
      "    22494 x 22494 diagonal covariance (kind = 6) found.\n",
      "    Orientation priors read.\n",
      "    22494 x 22494 diagonal covariance (kind = 5) found.\n",
      "    Depth priors read.\n",
      "    Did not find the desired covariance matrix (kind = 3)\n",
      "    Reading a source space...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Distance information added...\n",
      "    [done]\n",
      "    Reading a source space...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Distance information added...\n",
      "    [done]\n",
      "    2 source spaces read\n",
      "    Read a total of 4 projection items:\n",
      "        PCA-v1 (1 x 102) active\n",
      "        PCA-v2 (1 x 102) active\n",
      "        PCA-v3 (1 x 102) active\n",
      "        Average EEG reference (1 x 60) active\n",
      "    Source spaces transformed to the inverse solution coordinate frame\n",
      "Opening raw data file /home/reallocing/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...\n",
      "    Read a total of 4 projection items:\n",
      "        PCA-v1 (1 x 102)  idle\n",
      "        PCA-v2 (1 x 102)  idle\n",
      "        PCA-v3 (1 x 102)  idle\n",
      "        Average EEG reference (1 x 60)  idle\n",
      "    Range : 6450 ... 48149 =     42.956 ...   320.665 secs\n",
      "Ready.\n",
      "Current compensation grade : 0\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "inverse_operator = read_inverse_operator(fname_inv)\n",
    "label = mne.read_label(fname_label)\n",
    "raw = mne.io.read_raw_fif(fname_raw)\n",
    "events = mne.read_events(fname_event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02174, -0.01283, -0.01217],\n",
       "       [-0.02165, -0.01262, -0.01242],\n",
       "       [-0.02133, -0.01223, -0.01194],\n",
       "       ..., \n",
       "       [-0.02196,  0.01411, -0.02296],\n",
       "       [-0.02242,  0.01382, -0.02308],\n",
       "       [-0.02276,  0.01343, -0.02312]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up pick list\n",
    "include = []\n",
    "raw.info['bads'] += ['EEG 053']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pick MEG channels\n",
    "picks = mne.pick_types(raw.info,meg=True,eeg=False,stim=False,eog=True,include=include,exclude='bads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72 matching events found\n",
      "Applying baseline correction (mode: mean)\n",
      "Not setting metadata\n",
      "Created an SSP operator (subspace dimension = 3)\n",
      "4 projection items activated\n"
     ]
    }
   ],
   "source": [
    "# read epochs\n",
    "epochs = mne.Epochs(raw,events,event_id,tmin,tmax,picks=picks,baseline=(None,0),reject=dict(mag=4e-12,grad=4000e-13,eog=150e-6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on MAG : [u'MEG 1711']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n"
     ]
    }
   ],
   "source": [
    "# Get evoked data (averaging across trials in sensor space)\n",
    "evoked = epochs.average()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing the inverse operator for use...\n",
      "    Scaled noise and source covariance from nave = 1 to nave = 55\n",
      "    Created the regularized inverter\n",
      "    Created an SSP operator (subspace dimension = 3)\n",
      "    Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)\n",
      "    Computing noise-normalization factors (dSPM)...\n",
      "[done]\n",
      "Picked 305 channels from the data\n",
      "Computing inverse...\n",
      "    Eigenleads need to be weighted ...\n",
      "Processing epoch : 1\n",
      "Processing epoch : 2\n",
      "Processing epoch : 3\n",
      "Processing epoch : 4\n",
      "Processing epoch : 5\n",
      "Processing epoch : 6\n",
      "Processing epoch : 7\n",
      "Processing epoch : 8\n",
      "Processing epoch : 9\n",
      "Processing epoch : 10\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 11\n",
      "Processing epoch : 12\n",
      "Processing epoch : 13\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 14\n",
      "Processing epoch : 15\n",
      "Processing epoch : 16\n",
      "Processing epoch : 17\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 18\n",
      "Processing epoch : 19\n",
      "Processing epoch : 20\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 21\n",
      "Processing epoch : 22\n",
      "Processing epoch : 23\n",
      "    Rejecting  epoch based on MAG : [u'MEG 1711']\n",
      "Processing epoch : 24\n",
      "Processing epoch : 25\n",
      "Processing epoch : 26\n",
      "Processing epoch : 27\n",
      "Processing epoch : 28\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 29\n",
      "Processing epoch : 30\n",
      "Processing epoch : 31\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 32\n",
      "Processing epoch : 33\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 34\n",
      "Processing epoch : 35\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 36\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 37\n",
      "Processing epoch : 38\n",
      "Processing epoch : 39\n",
      "Processing epoch : 40\n",
      "Processing epoch : 41\n",
      "Processing epoch : 42\n",
      "Processing epoch : 43\n",
      "Processing epoch : 44\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 45\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 46\n",
      "Processing epoch : 47\n",
      "Processing epoch : 48\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "Processing epoch : 49\n",
      "Processing epoch : 50\n",
      "Processing epoch : 51\n",
      "Processing epoch : 52\n",
      "Processing epoch : 53\n",
      "Processing epoch : 54\n",
      "Processing epoch : 55\n",
      "[done]\n",
      "Preparing the inverse operator for use...\n",
      "    Scaled noise and source covariance from nave = 1 to nave = 55\n",
      "    Created the regularized inverter\n",
      "    Created an SSP operator (subspace dimension = 3)\n",
      "    Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)\n",
      "    Computing noise-normalization factors (dSPM)...\n",
      "[done]\n",
      "Applying inverse operator to \"1\"...\n",
      "    Picked 305 channels from the data\n",
      "    Computing inverse...\n",
      "    Eigenleads need to be weighted ...\n",
      "    Computing residual...\n",
      "    Explained  59.3% variance\n",
      "    dSPM...\n",
      "[done]\n"
     ]
    }
   ],
   "source": [
    "# Compute inverse solution and stcs for each epoch\n",
    "# Use the same inverse operator as with evoked data (i.e., set nave)\n",
    "# If you use a different nave, dSPM just scales by a factor sqrt(nave)\n",
    "stcs = apply_inverse_epochs(epochs,inverse_operator,lambda2,method,label,pick_ori='normal',nave=evoked.nave)\n",
    "stc_evoked = apply_inverse(evoked,inverse_operator,lambda2,method,pick_ori='normal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "stc_evoked_label = stc_evoked.in_label(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SourceEstimate  |  33 vertices, subject : sample, tmin : -199.795213158 (ms), tmax : 499.488032896 (ms), tstep : 6.65984043861 (ms), data shape : (33, 106)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stc_evoked_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing the inverse operator for use...\n",
      "    Scaled noise and source covariance from nave = 1 to nave = 55\n",
      "    Created the regularized inverter\n",
      "    Created an SSP operator (subspace dimension = 3)\n",
      "    Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)\n",
      "    Computing noise-normalization factors (dSPM)...\n",
      "[done]\n",
      "Applying inverse operator to \"1\"...\n",
      "    Picked 305 channels from the data\n",
      "    Computing inverse...\n",
      "    Eigenleads need to be weighted ...\n",
      "    Computing residual...\n",
      "    Explained  59.3% variance\n",
      "    dSPM...\n",
      "[done]\n"
     ]
    }
   ],
   "source": [
    "# Mean across trials but not across vertices in label\n",
    "mean_stc = sum(stcs) / len(stcs)\n",
    "\n",
    "# compute sign flip to avoid signal cancellation when averaging signed values\n",
    "flip = mne.label_sign_flip(label, inverse_operator['src'])\n",
    "\n",
    "label_mean = np.mean(mean_stc.data, axis=0)\n",
    "label_mean_flip = np.mean(flip[:, np.newaxis] * mean_stc.data, axis=0)\n",
    "\n",
    "# Get inverse solution by inverting evoked data\n",
    "stc_evoked = apply_inverse(evoked, inverse_operator, lambda2, method,\n",
    "                           pick_ori=\"normal\")\n",
    "\n",
    "# apply_inverse() does whole brain, so sub-select label of interest\n",
    "stc_evoked_label = stc_evoked.in_label(label)\n",
    "\n",
    "# Average over label (not caring to align polarities here)\n",
    "label_mean_evoked = np.mean(stc_evoked_label.data, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa311799e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## View activation time-series to illustrate the benefit of aligning/flipping\n",
    "times = 1e3 * stcs[0].times  # times in ms\n",
    "\n",
    "plt.figure()\n",
    "h0 = plt.plot(times, mean_stc.data.T, 'k')\n",
    "h1, = plt.plot(times, label_mean, 'r', linewidth=3)\n",
    "h2, = plt.plot(times, label_mean_flip, 'g', linewidth=3)\n",
    "plt.legend((h0[0], h1, h2), ('all dipoles in label', 'mean',\n",
    "                             'mean with sign flip'))\n",
    "plt.xlabel('time (ms)')\n",
    "plt.ylabel('dSPM value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}