Statistical_analysis_EEG_own_exp.ipynb

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    "# EEG analysis - Neuro Science 2022\n",
    "### Full analysis of digit experiment - \n",
    "#### Cluster Based Permutation testing and T-test\n",
    "This script contains the workflow of EEG statistical testing of the Neuro Science Digit Experiment using Cluster Based Permutation testing and T-testing.\n",
    "\n",
    "\n",
    "#### Introduction of Experiment\n",
    "In this experiment we will investigate the predictive processing of the brain using EEG.\n",
    "\n",
    "The experiment will consist of 2 different phases:\n",
    "\n",
    "In phase 1, the participants will throughout 30 rounds be presented to a sequence of numbers containing 5 digits (e.g. 1,2,3,4,5). The participant is instructed to memorise the sequence of numbers in that specific order of the sequence.\n",
    "\n",
    "In phase 2, the participant will again be shown 5 numbers in a sequence displayed on at a time throughout 90 trials. These new sequences of numbers are either manipulated in the beginning or in the end of the sequence (e.g. 1,8,3,4,5 or 1,2,3,8,5 ), or are similar to the first memorised sequence. Thus, we have 30 trials of sequences of 5 digits with a manipulation in the beginning (e.g 1,8,3,4,5), 30 trials of sequences of 5 digits with a manipulation in the end of the sequence (1,2,3,8,5 ), and 30 trials similar to the memorised sequence in phase 1 (1,2,3,4,5). The participant is instructed to report whether the sequences of numbers in phase 2 are equal to or different from the memorised sequence from phase 1 (1,2,3,4,5) as soon as they experience the sequence being different or equal.\n",
    "\n",
    "When measuring the neural signals using EEG, we generally expect to see a spike of activity when each number is read. Moreover, we expect to see a greater spike of activity in the visual cortex when the sequence of numbers are manipulated in the beginning(1,8,3,4,5 ), and to see an even greater spike of activity in the visual cortex when the sequence of numbers are manipulated in the end (1,2,3,8,5 ).\n",
    "\n",
    "We expect to see increased activity in visual cortex when the one number in the sequence deviates from the anticipation of the participant, this being an anticipation we induced in the participant in phase 1. Thus, the participant expects to see the memorised sequence of numbers, and then experiences a moment of “surprise” when seeing a number that does not fit in the memorised sequence, and this will be evident in the EEG measures."
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    "%%capture\n",
    "\n",
    "# importing modules\n",
    "import os\n",
    "import pip\n",
    "os.system('python -m pip install mne')\n",
    "os.system('python -m pip install scikit-learn')\n",
    "import numpy as np\n",
    "!pip install pandas\n",
    "from pandas import *\n",
    "import mne\n",
    "from scipy import stats as st\n",
    "import statistics as stats\n",
    "from itertools import combinations\n",
    "import matplotlib.pyplot as plt\n",
    "import mne\n",
    "import random\n",
    "\n",
    "!python -m pip install statsmodels\n",
    "from statsmodels.stats.multitest import multipletests as multi"
   ]
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   "source": [
    "## Read data (epochs saved from previous analysis)"
   ]
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     "text": [
      "Reading /work/RikkeUldbæk#3267/fif_files/all_epochs_eeg.fif ...\n",
      "    Found the data of interest:\n",
      "        t =    -200.00 ...     496.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "0 bad epochs dropped\n",
      "Not setting metadata\n",
      "559 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n"
     ]
    },
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     "name": "stderr",
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     "text": [
      "/tmp/ipykernel_192/1892190047.py:2: RuntimeWarning: This filename (/work/RikkeUldbæk#3267/fif_files/all_epochs_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs('/work/RikkeUldbæk#3267/fif_files/all_epochs_eeg.fif')\n"
     ]
    }
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   "source": [
    "# read the epochs\n",
    "epochs = mne.read_epochs('/work/RikkeUldbæk#3267/fif_files/all_epochs_eeg.fif')\n",
    "\n",
    "# extracting the epochs we need (i.e wrong 3rd place, wrong 5th place, correct)\n",
    "incorr_2 = epochs['recog_phase/incorr2/second']\n",
    "incorr_4 = epochs['recog_phase/incorr4/fourth']\n",
    "correct = epochs['recog_phase/corr/fifth'] ### AT FIFTH to have an equal amount of elements, justify more???\n",
    "\n",
    "\n",
    "# creating evoked for incorrect 3rd place\n",
    "incorr_2_evoked = incorr_2.average()\n",
    "\n",
    "# creating evoked for incorrect 5th place\n",
    "incorr_4_evoked = incorr_4.average()\n",
    "\n",
    "#  creating evoked for correct\n",
    "correct_evoked = correct.average()\n",
    "\n",
    "\n",
    "# Merging the 3 evokeds together\n",
    "all_evokeds = dict(incorr2=incorr_2_evoked, incorr4= incorr_4_evoked, correct = correct_evoked)\n",
    "\n",
    "#set seed\n",
    "random.seed(1)"
   ]
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    "## Windowed Mean\n",
    "We've got 3 conditions of interest: \n",
    "- trials with incorrect digit in 2nd place\n",
    "- trials with incorrect digit in 4th place \n",
    "\n",
    "One of the simplest way in which we can determine whether the signal in our conditions are statistically significant is by:\n",
    "\n",
    "Segmenting our data using only certain channels in a specific time window. Keep in mind that which time window and channels should be established a priori, for instance according to the literature.\n",
    "Taking the mean of that window across channels and trials and samples.\n",
    "Running statistical tests on the windowed means from the two conditions.\n",
    "In an experiment with multiple participants we would also average over trials from individual participants, in order to only have one data point per participant (and thereby avoid multiple comparisons). However, since we have one participant, we can keep one dimension of the individual data, i.e. the trials."
   ]
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    "## T-Test\n",
    "In this analysis, we will perform 3 t-tests to establish whether the means of the two groups are statistically significant. One on Incorrect 2 and Correct trials, another on Incorrect 4 and Correct trials, and lastly a t-test on Incorrect 2nd place trials and Incorrect 4th place trials. \n",
    "\n",
    "We can use the get_data() function to get the numerical values of the signal (in microvolts) for the t-test. tmin and tmax are used to define the size of the window, and the picks are the channels that we expect to see an effect in.\n"
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     "text": [
      "(27, 4, 25)\n",
      "(26, 4, 25)\n",
      "(31, 4, 25)\n"
     ]
    }
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   "source": [
    "# P300 electrodes,  picks=['Fz', 'Oz','Cz','Pz', 'P3']\n",
    "incor2_data_P300 = incorr_2.get_data(picks=['Fz', 'Oz','Cz','Pz', 'P3'], tmin=.1, tmax=.2)\n",
    "print(incor2_data.shape)\n",
    "\n",
    "incor4_data_P300 = incorr_4.get_data(picks=['Fz', 'Oz','Cz','Pz', 'P3'], tmin=.1, tmax=.2)\n",
    "print(incor4_data.shape)\n",
    "\n",
    "corr_data_P300 = correct.get_data(picks=['Fz', 'Oz','Cz','Pz', 'P3'], tmin=.1, tmax=.2)\n",
    "print(corr_data.shape)"
   ]
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   "source": [
    "Now we can average over the data so we only have the trials dimension."
   ]
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    "incor2_mean_P300 = np.mean(incor2_data_P300, axis=2) # averaging over the third dimension of the data\n",
    "incor2_mean_P300 = np.mean(incor2_mean_P300, axis=1) # averaging over the second dimension of the data\n",
    "\n",
    "incor4_mean_P300 = np.mean(incor4_data_P300, axis=2)\n",
    "incor4_mean_P300 = np.mean(incor4_mean_P300, axis=1)\n",
    "\n",
    "corr_mean_P300 = np.mean(corr_data_P300, axis=2)\n",
    "corr_mean_P300 = np.mean(corr_mean_P300, axis=1)"
   ]
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    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.984584421570365, pvalue=0.329476342456719)"
      ]
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   "source": [
    "from scipy import stats as st\n",
    "import statistics as stats\n",
    "\n",
    "#T -test on incorrect 2 and incorrect 4\n",
    "st.ttest_ind(a=incor2_mean_P300, b=incor4_mean_P300)"
   ]
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       "Ttest_indResult(statistic=0.7492819497186887, pvalue=0.456880142063108)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#T -test on incorrect 4 and correct\n",
    "st.ttest_ind(a=incor4_mean_P300, b=corr_mean_P300)"
   ]
  },
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       "Ttest_indResult(statistic=1.6902665365665974, pvalue=0.09653683515798962)"
      ]
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   "source": [
    "#T -test on incorrect 2 and correct\n",
    "st.ttest_ind(a=incor2_mean_P300, b=corr_mean_P300)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### T-test for MMN\n"
   ]
  },
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   "id": "3bf7117a-36ff-42c5-9310-d8718ba408eb",
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     "text": [
      "(27, 4, 25)\n",
      "(26, 4, 25)\n",
      "(31, 4, 25)\n"
     ]
    }
   ],
   "source": [
    "# vMMN electrodes,  picks=['Pz', 'Oz','P7','P8']\n",
    "incor2_data = incorr_2.get_data(picks=['Pz', 'Oz','P7','P8'], tmin=.1, tmax=.2)\n",
    "print(incor2_data.shape)\n",
    "\n",
    "incor4_data = incorr_4.get_data(picks=['Pz', 'Oz','P7','P8'], tmin=.1, tmax=.2)\n",
    "print(incor4_data.shape)\n",
    "\n",
    "corr_data = correct.get_data(picks=['Pz', 'Oz','P7','P8'], tmin=.1, tmax=.2)\n",
    "print(corr_data.shape)"
   ]
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    {
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     "text": [
      "(27,)\n",
      "(26,)\n",
      "(31,)\n"
     ]
    }
   ],
   "source": [
    "incor2_mean = np.mean(incor2_data, axis=2) # averaging over the third dimension of the data\n",
    "incor2_mean = np.mean(incor2_mean, axis=1) # averaging over the second dimension of the data\n",
    "\n",
    "incor4_mean = np.mean(incor4_data, axis=2)\n",
    "incor4_mean = np.mean(incor4_mean, axis=1)\n",
    "\n",
    "corr_mean = np.mean(corr_data, axis=2)\n",
    "corr_mean = np.mean(corr_mean, axis=1)\n",
    "\n",
    "print(incor2_mean.shape)\n",
    "print(incor4_mean.shape)\n",
    "print(corr_mean.shape)"
   ]
  },
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    {
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       "Ttest_indResult(statistic=-0.25995085293723547, pvalue=0.7959482823529211)"
      ]
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   "source": [
    "#T -test on incorrect 2 and incorrect 4\n",
    "st.ttest_ind(a=incor2_mean, b=incor4_mean)"
   ]
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       "Ttest_indResult(statistic=1.3628822327401882, pvalue=0.17847378577503065)"
      ]
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   ],
   "source": [
    "#T -test on incorrect 4 and correct\n",
    "st.ttest_ind(a=incor4_mean, b=corr_mean)"
   ]
  },
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   "source": [
    "#T -test on incorrect 2 and correct\n",
    "st.ttest_ind(a=incor2_mean, b=corr_mean)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Cluster based Permutation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5768a6a3-0726-4777-aa5b-56b4ab3eac71",
   "metadata": {},
   "source": [
    "## 1. Set threshold for t-values (contrast) for clustering\n",
    "H0: there is no diffference in neural activity between seeing an incorrect digit at place 2 and place 4 in the sequence. \n"
   ]
  },
  {
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   "id": "9257fba1-deaa-4932-b97b-fe80c2b157ba",
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    "execution": {
     "iopub.execute_input": "2022-05-25T12:31:46.353159Z",
     "iopub.status.busy": "2022-05-25T12:31:46.352655Z",
     "iopub.status.idle": "2022-05-25T12:31:46.379800Z",
     "shell.execute_reply": "2022-05-25T12:31:46.378483Z",
     "shell.execute_reply.started": "2022-05-25T12:31:46.353105Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27, 30, 175)\n",
      "(27, 175, 30)\n"
     ]
    }
   ],
   "source": [
    "# getting the data from incorr 2nd, incorr 4th and correct condition\n",
    "X = [epochs[k].get_data() for k in ['recog_phase/incorr2/second', 'recog_phase/incorr4/second', 'recog_phase/corr/fifth']]\n",
    "print(X[0].shape)\n",
    "\n",
    "# transposing\n",
    "X = [np.transpose(x, (0, 2, 1)) for x in X]\n",
    "print(X[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f0d58e13-6d7e-48f1-9413-d5e0db6ea474",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T12:31:48.440744Z",
     "iopub.status.busy": "2022-05-25T12:31:48.440214Z",
     "iopub.status.idle": "2022-05-25T12:31:48.742689Z",
     "shell.execute_reply": "2022-05-25T12:31:48.741889Z",
     "shell.execute_reply.started": "2022-05-25T12:31:48.440692Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not find a adjacency matrix for the data. Computing adjacency based on Delaunay triangulations.\n",
      "-- number of adjacent vertices : 30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Between-sensor adjacency')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding channel adjacency: informs us which channels are next to each other (for co-localisation)\n",
    "adjacency, ch_names = mne.channels.find_ch_adjacency(epochs.info, ch_type='eeg')\n",
    "\n",
    "# plotting between-sensor adjacency\n",
    "plt.imshow(adjacency.toarray(), cmap='gray',\n",
    "           interpolation='nearest')\n",
    "plt.xlabel('{} EEG'.format(len(ch_names)))\n",
    "plt.ylabel('{} EEG'.format(len(ch_names)))\n",
    "plt.title('Between-sensor adjacency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "197e65fe-e4fd-4bf3-968d-409022e6acb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T12:31:50.910780Z",
     "iopub.status.busy": "2022-05-25T12:31:50.910257Z",
     "iopub.status.idle": "2022-05-25T12:31:54.604226Z",
     "shell.execute_reply": "2022-05-25T12:31:54.602884Z",
     "shell.execute_reply.started": "2022-05-25T12:31:50.910726Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using a threshold of 3.109311\n",
      "stat_fun(H1): min=0.000201 max=13.188060\n",
      "Running initial clustering\n",
      "Found 63 clusters\n",
      "Permuting 1999 times...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_192/2478297223.py:5: RuntimeWarning: Ignoring argument \"tail\", performing 1-tailed F-test\n",
      "  cluster_stats = mne.stats.spatio_temporal_cluster_test(X, n_permutations=2000, tail=0,\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb2873293d4c4568a35ff5d8c43622b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          |  : 0/1999 [00:00<?,       ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing cluster p-values\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# set family-wise p-value\n",
    "p_accept = 0.05\n",
    "\n",
    "# running the permutation test with 2000 permutations and a random seed of 1\n",
    "cluster_stats = mne.stats.spatio_temporal_cluster_test(X, n_permutations=2000, tail=0,\n",
    "                                             n_jobs=-1, buffer_size=None, adjacency=adjacency, seed=1)\n",
    "\n",
    "# selecting clusters with significant p-values\n",
    "T_obs, clusters, p_values, _ = cluster_stats\n",
    "good_cluster_inds = np.where(p_values < p_accept)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "151c928f-e825-4ec0-b1c3-8ab762c5d62f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T12:31:56.586762Z",
     "iopub.status.busy": "2022-05-25T12:31:56.586234Z",
     "iopub.status.idle": "2022-05-25T12:31:56.595957Z",
     "shell.execute_reply": "2022-05-25T12:31:56.594664Z",
     "shell.execute_reply.started": "2022-05-25T12:31:56.586707Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.930e-01 1.000e+00 9.945e-01 1.000e+00 9.910e-01 1.000e+00 3.560e-01\n",
      " 1.000e+00 1.000e+00 1.000e+00 1.000e+00 9.995e-01 9.820e-01 1.000e+00\n",
      " 1.000e+00 1.000e+00 1.000e+00 1.000e+00 9.960e-01 1.000e+00 1.000e+00\n",
      " 4.830e-01 1.000e+00 3.840e-01 1.000e+00 1.000e+00 1.000e+00 1.000e+00\n",
      " 1.000e+00 1.000e+00 1.000e+00 6.865e-01 9.995e-01 1.000e+00 1.000e+00\n",
      " 1.000e+00 1.000e+00 9.490e-01 1.525e-01 1.000e+00 9.730e-01 5.000e-04\n",
      " 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00 9.490e-01\n",
      " 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00 9.995e-01\n",
      " 1.000e+00 1.990e-01 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00]\n",
      "[41]\n"
     ]
    }
   ],
   "source": [
    "print(p_values)\n",
    "print(good_cluster_inds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03bfa818-6a5e-4676-a105-8b1fe959c2e8",
   "metadata": {},
   "source": [
    "## Cluster-based permutation -  Incorr2 and Correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "27c17647-8570-4868-8c2a-296db9a0b7f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T12:31:59.997279Z",
     "iopub.status.busy": "2022-05-25T12:31:59.996756Z",
     "iopub.status.idle": "2022-05-25T12:32:00.564336Z",
     "shell.execute_reply": "2022-05-25T12:32:00.563520Z",
     "shell.execute_reply.started": "2022-05-25T12:31:59.997225Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27, 30, 175)\n",
      "(27, 175, 30)\n",
      "combining channels using \"gfp\"\n",
      "combining channels using \"gfp\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "#creating new adjacency matrix\n",
    "X = [epochs[k].get_data() for k in ['recog_phase/incorr2/second', 'recog_phase/corr/fifth']]\n",
    "print(X[0].shape)\n",
    "\n",
    "# transposing\n",
    "X = [np.transpose(x, (0, 2, 1)) for x in X]\n",
    "print(X[0].shape)\n",
    "\n",
    "\n",
    "# configuration of variables for visualisation\n",
    "colors = {\"incorr2\": \"crimson\", \"corr\": \"#005f6a\"}\n",
    "\n",
    "# organising data for plotting\n",
    "evokeds = {cond: epochs[cond].average() for cond in [\"recog_phase/incorr2/second\", \"recog_phase/corr/fifth\"]} \n",
    "\n",
    "# looping over clusters\n",
    "for i_clu, clu_idx in enumerate(good_cluster_inds):\n",
    "    # unpacking cluster information\n",
    "    time_inds, space_inds = np.squeeze(clusters[clu_idx])\n",
    "    ch_inds = np.unique(space_inds)\n",
    "    time_inds = np.unique(time_inds)\n",
    "\n",
    "    # topography for F stat\n",
    "    f_map = T_obs[time_inds, ...].mean(axis=0)\n",
    "\n",
    "    # getting signals at the sensors contributing to the cluster\n",
    "    sig_times = epochs.times[time_inds]\n",
    "\n",
    "    # creating spatial mask\n",
    "    mask = np.zeros((f_map.shape[0], 1), dtype=bool)\n",
    "    mask[ch_inds, :] = True\n",
    "\n",
    "    # initialising the figure\n",
    "    fig, ax_topo = plt.subplots(1, 1, figsize=(10, 3))\n",
    "\n",
    "    # plotting average test statistic and mark significant sensors\n",
    "    f_evoked = mne.EvokedArray(f_map[:, np.newaxis], epochs.info, tmin=0)\n",
    "    f_evoked.plot_topomap(times=0, mask=mask, axes=ax_topo, cmap='Reds',\n",
    "                          vmin=np.min, vmax=np.max, show=False,\n",
    "                          colorbar=False, mask_params=dict(markersize=10))\n",
    "    image = ax_topo.images[0]\n",
    "\n",
    "    # creating additional axes (for ERF and colorbar)\n",
    "    divider = make_axes_locatable(ax_topo)\n",
    "\n",
    "    # adding axes for colourbar\n",
    "    ax_colorbar = divider.append_axes('right', size='5%', pad=0.05)\n",
    "    plt.colorbar(image, cax=ax_colorbar)\n",
    "    ax_topo.set_xlabel(\n",
    "        'Averaged F-map ({:0.3f} - {:0.3f} s)'.format(*sig_times[[0, -1]]))\n",
    "\n",
    "    # adding new axis for time courses and plot time courses\n",
    "    ax_signals = divider.append_axes('right', size='300%', pad=1.2)\n",
    "    title = 'Cluster #{0}, {1} sensor'.format(i_clu + 1, len(ch_inds))\n",
    "    if len(ch_inds) > 1:\n",
    "        title += \"s (mean)\"\n",
    "    mne.viz.plot_compare_evokeds(evokeds, title=title, picks=ch_inds, axes=ax_signals,\n",
    "                         colors=colors, show=False,\n",
    "                         split_legend=True, truncate_yaxis='auto')\n",
    "\n",
    "    # plotting temporal cluster extent\n",
    "    ymin, ymax = ax_signals.get_ylim()\n",
    "    ax_signals.fill_betweenx((ymin, ymax), sig_times[0], sig_times[-1],\n",
    "                             color='orange', alpha=0.3)\n",
    "\n",
    "    # clean-up\n",
    "    mne.viz.tight_layout(fig=fig)\n",
    "    fig.subplots_adjust(bottom=.05)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad140f31-334b-4a1c-8d26-9a43f9392f65",
   "metadata": {},
   "source": [
    "STATISTICAL SIGNIFICANT DIFFERENECE IN THE YELLOW WINDOW,between the 3 conditions.  This cluster is based on 19 and 6 sensors out of the 30 sensors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b306f88-28c8-4af2-a80b-e6cf9d391b3b",
   "metadata": {},
   "source": [
    "## Cluster-based permuation - Incorr4 and Correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6be348fb-caf2-4ddf-8b69-12c10bc60cbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T12:32:17.585920Z",
     "iopub.status.busy": "2022-05-25T12:32:17.585402Z",
     "iopub.status.idle": "2022-05-25T12:32:21.660200Z",
     "shell.execute_reply": "2022-05-25T12:32:21.658910Z",
     "shell.execute_reply.started": "2022-05-25T12:32:17.585868Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26, 30, 175)\n",
      "(26, 175, 30)\n",
      "stat_fun(H1): min=0.000000 max=18.879643\n",
      "Running initial clustering\n",
      "Found 58 clusters\n",
      "Permuting 1999 times...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "280bec6080c843e3be383c6a3a458d22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          |  : 0/1999 [00:00<?,       ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing cluster p-values\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "X = [epochs[k].get_data() for k in ['recog_phase/incorr4/fourth', 'recog_phase/corr/fifth']]\n",
    "print(X[0].shape)\n",
    "\n",
    "# transposing\n",
    "X = [np.transpose(x, (0, 2, 1)) for x in X]\n",
    "print(X[0].shape)\n",
    "\n",
    "# set family-wise p-value\n",
    "p_accept = 0.05\n",
    "\n",
    "# running the permutation test with 2000 permutations and a random seed of 4\n",
    "cluster_stats = mne.stats.spatio_temporal_cluster_test(X, n_permutations=2000, tail=0,\n",
    "                                             n_jobs=-1, buffer_size=None, adjacency=adjacency, seed=4, threshold = 4)\n",
    "\n",
    "# selecting clusters with significant p-values\n",
    "T_obs, clusters, p_values, _ = cluster_stats\n",
    "good_cluster_inds = np.where(p_values < p_accept)[0]\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "# configuration of variables for visualisation\n",
    "colors = {\"corr\": \"crimson\", \"incorr4\": 'steelblue'}\n",
    "\n",
    "# organising data for plotting\n",
    "evokeds = {cond: epochs[cond].average() for cond in [\"recog_phase/corr/fifth\", \"recog_phase/incorr4/fourth\"]} \n",
    "\n",
    "\n",
    "# looping over clusters\n",
    "for i_clu, clu_idx in enumerate(good_cluster_inds):\n",
    "    # unpacking cluster information\n",
    "    time_inds, space_inds = np.squeeze(clusters[clu_idx])\n",
    "    ch_inds = np.unique(space_inds)\n",
    "    time_inds = np.unique(time_inds)\n",
    "\n",
    "    # topography for F stat\n",
    "    f_map = T_obs[time_inds, ...].mean(axis=0)\n",
    "\n",
    "    # getting signals at the sensors contributing to the cluster\n",
    "    sig_times = epochs.times[time_inds]\n",
    "\n",
    "    # creating spatial mask\n",
    "    mask = np.zeros((f_map.shape[0], 1), dtype=bool)\n",
    "    mask[ch_inds, :] = True\n",
    "\n",
    "    # initialising the figure\n",
    "    fig, ax_topo = plt.subplots(1, 1, figsize=(10, 3))\n",
    "\n",
    "    # plotting average test statistic and mark significant sensors\n",
    "    f_evoked = mne.EvokedArray(f_map[:, np.newaxis], epochs.info, tmin=0)\n",
    "    f_evoked.plot_topomap(times=0, mask=mask, axes=ax_topo, cmap='Reds',\n",
    "                          vmin=np.min, vmax=np.max, show=False,\n",
    "                          colorbar=False, mask_params=dict(markersize=10))\n",
    "    image = ax_topo.images[0]\n",
    "\n",
    "    # creating additional axes (for ERF and colorbar)\n",
    "    divider = make_axes_locatable(ax_topo)\n",
    "\n",
    "    # adding axes for colourbar\n",
    "    ax_colorbar = divider.append_axes('right', size='5%', pad=0.05)\n",
    "    plt.colorbar(image, cax=ax_colorbar)\n",
    "    ax_topo.set_xlabel(\n",
    "        'Averaged F-map ({:0.3f} - {:0.3f} s)'.format(*sig_times[[0, -1]]))\n",
    "\n",
    "    # adding new axis for time courses and plot time courses\n",
    "    ax_signals = divider.append_axes('right', size='300%', pad=1.2)\n",
    "    title = 'Cluster #{0}, {1} sensor'.format(i_clu + 1, len(ch_inds))\n",
    "    if len(ch_inds) > 1:\n",
    "        title += \"s (mean)\"\n",
    "    mne.viz.plot_compare_evokeds(evokeds, title=title, picks=ch_inds, axes=ax_signals,\n",
    "                         colors=colors, show=False,\n",
    "                         split_legend=True, truncate_yaxis='auto')\n",
    "\n",
    "    # plotting temporal cluster extent\n",
    "    ymin, ymax = ax_signals.get_ylim()\n",
    "    ax_signals.fill_betweenx((ymin, ymax), sig_times[0], sig_times[-1],\n",
    "                             color='orange', alpha=0.3)\n",
    "\n",
    "    # clean-up\n",
    "    mne.viz.tight_layout(fig=fig)\n",
    "    fig.subplots_adjust(bottom=.05)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c4f953-95e9-4c98-8810-5fbf69dda99b",
   "metadata": {},
   "source": [
    "## 1.1 Incorr2 and Incorr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "13bdd35f-f639-4687-a012-54dbf2ffa962",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T12:32:55.158325Z",
     "iopub.status.busy": "2022-05-25T12:32:55.157819Z",
     "iopub.status.idle": "2022-05-25T12:32:59.672333Z",
     "shell.execute_reply": "2022-05-25T12:32:59.671217Z",
     "shell.execute_reply.started": "2022-05-25T12:32:55.158269Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27, 30, 175)\n",
      "(27, 175, 30)\n",
      "stat_fun(H1): min=0.000000 max=24.052514\n",
      "Running initial clustering\n",
      "Found 57 clusters\n",
      "Permuting 1999 times...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "386435aec3ec4a77acb8c88676b3702b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          |  : 0/1999 [00:00<?,       ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing cluster p-values\n",
      "Done.\n",
      "combining channels using \"gfp\"\n",
      "combining channels using \"gfp\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combining channels using \"gfp\"\n",
      "combining channels using \"gfp\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = [epochs[k].get_data() for k in ['recog_phase/incorr2/second', 'recog_phase/incorr4/fourth']]\n",
    "print(X[0].shape)\n",
    "\n",
    "# transposing\n",
    "X = [np.transpose(x, (0, 2, 1)) for x in X]\n",
    "print(X[0].shape)\n",
    "\n",
    "\n",
    "# set family-wise p-value\n",
    "p_accept = 0.05\n",
    "\n",
    "# running the permutation test with 2000 permutations and a random seed of 4\n",
    "cluster_stats = mne.stats.spatio_temporal_cluster_test(X, n_permutations=2000, tail=0,\n",
    "                                             n_jobs=-1, buffer_size=None, adjacency=adjacency, seed=4, threshold = 4)\n",
    "\n",
    "# selecting clusters with significant p-values\n",
    "T_obs, clusters, p_values, _ = cluster_stats\n",
    "good_cluster_inds = np.where(p_values < p_accept)[0]\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "# configuration of variables for visualisation\n",
    "colors = {\"incorr2\": \"crimson\", \"incorr4\": 'steelblue'}\n",
    "\n",
    "# organising data for plotting\n",
    "evokeds = {cond: epochs[cond].average() for cond in [\"recog_phase/incorr2/second\", \"recog_phase/incorr4/fourth\"]} \n",
    "\n",
    "\n",
    "# looping over clusters\n",
    "for i_clu, clu_idx in enumerate(good_cluster_inds):\n",
    "    # unpacking cluster information\n",
    "    time_inds, space_inds = np.squeeze(clusters[clu_idx])\n",
    "    ch_inds = np.unique(space_inds)\n",
    "    time_inds = np.unique(time_inds)\n",
    "\n",
    "    # topography for F stat\n",
    "    f_map = T_obs[time_inds, ...].mean(axis=0)\n",
    "\n",
    "    # getting signals at the sensors contributing to the cluster\n",
    "    sig_times = epochs.times[time_inds]\n",
    "\n",
    "    # creating spatial mask\n",
    "    mask = np.zeros((f_map.shape[0], 1), dtype=bool)\n",
    "    mask[ch_inds, :] = True\n",
    "\n",
    "    # initialising the figure\n",
    "    fig, ax_topo = plt.subplots(1, 1, figsize=(10, 3))\n",
    "\n",
    "    # plotting average test statistic and mark significant sensors\n",
    "    f_evoked = mne.EvokedArray(f_map[:, np.newaxis], epochs.info, tmin=0)\n",
    "    f_evoked.plot_topomap(times=0, mask=mask, axes=ax_topo, cmap='Reds',\n",
    "                          vmin=np.min, vmax=np.max, show=False,\n",
    "                          colorbar=False, mask_params=dict(markersize=10))\n",
    "    image = ax_topo.images[0]\n",
    "\n",
    "    # creating additional axes (for ERF and colorbar)\n",
    "    divider = make_axes_locatable(ax_topo)\n",
    "\n",
    "    # adding axes for colourbar\n",
    "    ax_colorbar = divider.append_axes('right', size='5%', pad=0.05)\n",
    "    plt.colorbar(image, cax=ax_colorbar)\n",
    "    ax_topo.set_xlabel(\n",
    "        'Averaged F-map ({:0.3f} - {:0.3f} s)'.format(*sig_times[[0, -1]]))\n",
    "\n",
    "    # adding new axis for time courses and plot time courses\n",
    "    ax_signals = divider.append_axes('right', size='300%', pad=1.2)\n",
    "    title = 'Cluster #{0}, {1} sensor'.format(i_clu + 1, len(ch_inds))\n",
    "    if len(ch_inds) > 1:\n",
    "        title += \"s (mean)\"\n",
    "    mne.viz.plot_compare_evokeds(evokeds, title=title, picks=ch_inds, axes=ax_signals,\n",
    "                         colors=colors, show=False,\n",
    "                         split_legend=True, truncate_yaxis='auto')\n",
    "\n",
    "    # plotting temporal cluster extent\n",
    "    ymin, ymax = ax_signals.get_ylim()\n",
    "    ax_signals.fill_betweenx((ymin, ymax), sig_times[0], sig_times[-1],\n",
    "                             color='orange', alpha=0.3)\n",
    "\n",
    "    # clean-up\n",
    "    mne.viz.tight_layout(fig=fig)\n",
    "    fig.subplots_adjust(bottom=.05)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9292dabd-efb6-4577-a2ff-6faa07328422",
   "metadata": {},
   "source": [
    "# Pariwise t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5f4c991c-d891-49db-a431-8c0f015c81e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T13:42:59.928441Z",
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     "iopub.status.idle": "2022-05-25T13:42:59.942534Z",
     "shell.execute_reply": "2022-05-25T13:42:59.941633Z",
     "shell.execute_reply.started": "2022-05-25T13:42:59.928370Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def pairwise_Ttest(Epoch_Condition, tmin, tmax, channels, Epoch_Names):\n",
    "    n_conditions = range(len(Epoch_Condition))\n",
    "    data = []\n",
    "    for i in n_conditions:\n",
    "        data.append(Epoch_Condition[i].get_data(picks=channels, tmin=tmin, tmax=tmax))\n",
    "    \n",
    "    means = []\n",
    "    for i in n_conditions:\n",
    "        mean_temp = np.mean(data[i], axis=2)\n",
    "        means.append(np.mean(mean_temp, axis=1))\n",
    "    \n",
    "    ## create empty lists\n",
    "    pairwise_names = []\n",
    "    pairwise_pval = []\n",
    "    pairwise_tstat = []\n",
    "    \n",
    "    ## make all the combinations\n",
    "    for a, b in combinations(Epoch_Names, 2): \n",
    "        y = f\"{a} vs. {b}\"\n",
    "        pairwise_names.append(y)\n",
    "        \n",
    "    for a, b in combinations(means, 2): # function which make all combinations of 2 means possible\n",
    "        y = st.ttest_ind(a, b)\n",
    "        pairwise_pval.append(y.pvalue)\n",
    "        pairwise_tstat.append(y.statistic)\n",
    "    \n",
    "    \n",
    "    bonferroni = multi(pairwise_pval, alpha=0.05, method='bonferroni', is_sorted=False, returnsorted=False)\n",
    "    \n",
    "    ## create dataframe\n",
    "    data = DataFrame({\"Comparison\":pairwise_names, \"P-Value\":pairwise_pval, \"t-statistic\":pairwise_tstat, \"Bonferroni Adj.\":bonferroni[1]})\n",
    "    \n",
    "    return data\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96fea99d-f8e2-4094-8bef-e978623b1804",
   "metadata": {},
   "source": [
    "### Pairwise t-test for vMMN ERP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "eb69fb2a-11f2-49a2-bb57-f2a9f4241220",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T13:43:01.670109Z",
     "iopub.status.busy": "2022-05-25T13:43:01.669599Z",
     "iopub.status.idle": "2022-05-25T13:43:01.930768Z",
     "shell.execute_reply": "2022-05-25T13:43:01.929365Z",
     "shell.execute_reply.started": "2022-05-25T13:43:01.670055Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>P-Value</th>\n",
       "      <th>t-statistic</th>\n",
       "      <th>Bonferroni Adj.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>incorr_2 vs. incorr_4</td>\n",
       "      <td>0.411707</td>\n",
       "      <td>0.827682</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>incorr_2 vs. correct</td>\n",
       "      <td>0.357848</td>\n",
       "      <td>0.927110</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>incorr_4 vs. correct</td>\n",
       "      <td>0.943378</td>\n",
       "      <td>0.071351</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Comparison   P-Value  t-statistic  Bonferroni Adj.\n",
       "0  incorr_2 vs. incorr_4  0.411707     0.827682              1.0\n",
       "1   incorr_2 vs. correct  0.357848     0.927110              1.0\n",
       "2   incorr_4 vs. correct  0.943378     0.071351              1.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairwise = pairwise_Ttest(Epoch_Condition = [incorr_2, incorr_4, correct], \n",
    "             tmin = .100, \n",
    "             tmax = .250,\n",
    "             channels = ['Pz', 'Oz','P7','P8'], # MMN\n",
    "             Epoch_Names = [\"incorr_2\", \"incorr_4\", \"correct\"]\n",
    "              )\n",
    "\n",
    "pairwise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5413823-e7e8-4cec-9aac-1c83c2365d5e",
   "metadata": {},
   "source": [
    "### Pairwise t-test for P300 ERP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "7642a149-db08-4724-817b-8cdf838e1c47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-25T13:45:04.567583Z",
     "iopub.status.busy": "2022-05-25T13:45:04.567082Z",
     "iopub.status.idle": "2022-05-25T13:45:04.586277Z",
     "shell.execute_reply": "2022-05-25T13:45:04.585336Z",
     "shell.execute_reply.started": "2022-05-25T13:45:04.567530Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comparison</th>\n",
       "      <th>P-Value</th>\n",
       "      <th>t-statistic</th>\n",
       "      <th>Bonferroni Adj.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>incorr_2 vs. incorr_4</td>\n",
       "      <td>0.342605</td>\n",
       "      <td>-0.957954</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>incorr_2 vs. correct</td>\n",
       "      <td>0.296741</td>\n",
       "      <td>1.053270</td>\n",
       "      <td>0.890223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>incorr_4 vs. correct</td>\n",
       "      <td>0.043604</td>\n",
       "      <td>2.065466</td>\n",
       "      <td>0.130812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Comparison   P-Value  t-statistic  Bonferroni Adj.\n",
       "0  incorr_2 vs. incorr_4  0.342605    -0.957954         1.000000\n",
       "1   incorr_2 vs. correct  0.296741     1.053270         0.890223\n",
       "2   incorr_4 vs. correct  0.043604     2.065466         0.130812"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairwise = pairwise_Ttest(Epoch_Condition = [incorr_2, incorr_4, correct], \n",
    "             tmin = .300, \n",
    "             tmax = .600,\n",
    "             channels = ['Fz', 'Oz','Cz','Pz', 'P3'], # P300\n",
    "             Epoch_Names = [\"incorr_2\", \"incorr_4\", \"correct\"]\n",
    "              )\n",
    "\n",
    "pairwise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0647e80-9d1d-45d0-843e-3676fd38884c",
   "metadata": {},
   "source": [
    "The only statistically signitifact difference in amplitude is found between incorr4 and correct in the P300 ERP. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2d5137-86af-4c92-b18a-8960d1275b00",
   "metadata": {},
   "source": [
    "## Extracting data into CSV file\n",
    "For further analysis and plotting in R.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ab5431ad-95ae-458f-9759-88b3b2d23938",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-24T11:49:29.094073Z",
     "iopub.status.busy": "2022-05-24T11:49:29.093548Z",
     "iopub.status.idle": "2022-05-24T11:49:29.903547Z",
     "shell.execute_reply": "2022-05-24T11:49:29.902265Z",
     "shell.execute_reply.started": "2022-05-24T11:49:29.094017Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## csv for incorr 2\n",
    "shape = incor2_data.shape\n",
    "index = MultiIndex.from_product([range(s)for s in shape])\n",
    "incor2_1 = DataFrame({'incorr2_data': incor2_data.flatten()}, index=index).reset_index()\n",
    "incor2_1.to_csv('incorr2_data.csv', index=False)\n",
    "\n",
    "## csv for incorr 4\n",
    "shape = incor4_data.shape\n",
    "index = MultiIndex.from_product([range(s)for s in shape])\n",
    "incor4_1 = DataFrame({'incorr4_data': incor4_data .flatten()}, index=index).reset_index()\n",
    "incor4_1.to_csv('incorr4_data.csv', index=False)\n",
    "\n",
    "## csv for correct\n",
    "shape = corr_data.shape\n",
    "index = MultiIndex.from_product([range(s)for s in shape])\n",
    "corr_1 = DataFrame({'correct_data': corr_data .flatten()}, index=index).reset_index()\n",
    "corr_1.to_csv('correct_data.csv', index=False)\n"
   ]
  }
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