EEG_RDM_GIF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import squareform\n",
    "import imageio\n",
    "import seaborn as sns\n",
    "import matplotlib.animation as animation\n",
    "import pickle\n",
    "import glob\n",
    "import os\n",
    "import pickle\n",
    "import plotly.io as pio\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import os.path\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from itertools import product\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Naive Video RDMs for occipital region\n",
    "\n",
    "          \n",
    "windowed_eeg_rdm_dict_n_v = {}\n",
    "with h5py.File(\"/Users/huseyinelmas/Desktop/CCN-Lab/CCN-RSA/RSA_Results/Naive\"+\n",
    "               \"/Video/eegRDMs/eeg_rdm_naive_video_occipital_1_correlation.hdf5\", \"r\") as f:\n",
    "    attributes = list(f.attrs.items())\n",
    "    for key in list(f.keys()):\n",
    "          windowed_eeg_rdm_dict_n_v[tuple(map(int, key[1:-1].split(',')))] = np.asarray(f[key][\"rdm\"])\n",
    "\n",
    "stimuli =[\"robot-drink\",\"robot-grasp\",\"robot-handwave\",\"robot-talk\",\"robot-nudge\",\n",
    "        \"robot-paper\",\"robot-turn\",\"robot-wipe\",\"android-drink\",\"android-grasp\",\"android-handwave\",\n",
    "        \"android-talk\",\"android-nudge\",\"android-paper\",\"android-turn\",\"android-wipe\",\"human-drink\",\n",
    "        \"human-grasp\",\"human-handwave\",\"human-talk\",\"human-nudge\",\"human-paper\",\"human-turn\",\"human-wipe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "         \n",
    "windowed_eeg_rdm_dict_p_v = {}\n",
    "with h5py.File(\"/Users/huseyinelmas/Desktop/CCN-Lab/CCN-RSA/RSA_Results/Prior\"+\n",
    "               \"/Video/eegRDMs/eeg_rdm_prior_video_occipital_1_correlation.hdf5\", \"r\") as f:\n",
    "    attributes = list(f.attrs.items())\n",
    "    for key in list(f.keys()):\n",
    "          windowed_eeg_rdm_dict_p_v[tuple(map(int, key[1:-1].split(',')))] = np.asarray(f[key][\"rdm\"])\n",
    "\n",
    "stimuli =[\"robot-drink\",\"robot-grasp\",\"robot-handwave\",\"robot-talk\",\"robot-nudge\",\n",
    "        \"robot-paper\",\"robot-turn\",\"robot-wipe\",\"android-drink\",\"android-grasp\",\"android-handwave\",\n",
    "        \"android-talk\",\"android-nudge\",\"android-paper\",\"android-turn\",\"android-wipe\",\"human-drink\",\n",
    "        \"human-grasp\",\"human-handwave\",\"human-talk\",\"human-nudge\",\"human-paper\",\"human-turn\",\"human-wipe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "         \n",
    "windowed_eeg_rdm_dict_p_s = {}\n",
    "with h5py.File(\"/Users/huseyinelmas/Desktop/CCN-Lab/CCN-RSA/RSA_Results/Prior\"+\n",
    "               \"/Still/eegRDMs/eeg_rdm_prior_still_occipital_1_correlation.hdf5\", \"r\") as f:\n",
    "    attributes = list(f.attrs.items())\n",
    "    for key in list(f.keys()):\n",
    "          windowed_eeg_rdm_dict_p_s[tuple(map(int, key[1:-1].split(',')))] = np.asarray(f[key][\"rdm\"])\n",
    "\n",
    "stimuli =[\"robot-drink\",\"robot-grasp\",\"robot-handwave\",\"robot-talk\",\"robot-nudge\",\n",
    "        \"robot-paper\",\"robot-turn\",\"robot-wipe\",\"android-drink\",\"android-grasp\",\"android-handwave\",\n",
    "        \"android-talk\",\"android-nudge\",\"android-paper\",\"android-turn\",\"android-wipe\",\"human-drink\",\n",
    "        \"human-grasp\",\"human-handwave\",\"human-talk\",\"human-nudge\",\"human-paper\",\"human-turn\",\"human-wipe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Naive Still RDMs for occipital region\n",
    "\n",
    "\n",
    "windowed_eeg_rdm_dict_n_s = {}\n",
    "with h5py.File(\"/Users/huseyinelmas/Desktop/CCN-Lab/CCN-RSA/RSA_Results/Naive\"+\n",
    "               \"/Still/eegRDMs/eeg_rdm_naive_still_occipital_1_correlation.hdf5\", \"r\") as f:\n",
    "    attributes = list(f.attrs.items())\n",
    "    for key in list(f.keys()):\n",
    "          windowed_eeg_rdm_dict_n_s[tuple(map(int, key[1:-1].split(',')))] = np.asarray(f[key][\"rdm\"])\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inner(df_sorted):\n",
    "    start_times=[]\n",
    "    end_times=[]\n",
    "    sig_wind_times=[]\n",
    "    for i in range(df_sorted.shape[0]):\n",
    "        if(df_sorted.iloc[i:-1, :].shape[0] > 5):\n",
    "            if(( df_sorted.iloc[i+1].time == df_sorted.iloc[i].time+2) and \n",
    "               ( df_sorted.iloc[i+2].time == df_sorted.iloc[i+1].time+2) and\n",
    "               ( df_sorted.iloc[i+3].time == df_sorted.iloc[i+2].time+2) and\n",
    "               ( df_sorted.iloc[i+4].time == df_sorted.iloc[i+3].time+2) and\n",
    "               ( df_sorted.iloc[i-1].time != df_sorted.iloc[i].time-2)):\n",
    "                start_times.append(df_sorted.iloc[i].time) \n",
    "        if(df_sorted.iloc[:i, :].shape[0] > 5):\n",
    "            if(( df_sorted.iloc[i-1].time == df_sorted.iloc[i].time-2) and \n",
    "               ( df_sorted.iloc[i-2].time == df_sorted.iloc[i-1].time-2) and\n",
    "               ( df_sorted.iloc[i-3].time == df_sorted.iloc[i-2].time-2) and\n",
    "               ( df_sorted.iloc[i-4].time == df_sorted.iloc[i-3].time-2) and\n",
    "               (df_sorted.shape[0] <=i+1 or df_sorted.iloc[i+1].time != df_sorted.iloc[i].time+2 )):\n",
    "                end_times.append(df_sorted.iloc[i].time) \n",
    "        \n",
    "    return start_times, end_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def find_significant_rdms(df, correct=True):\n",
    "\n",
    "    pos_corr_rdms =  df[ df.kendall_tau > 0]\n",
    "    alpha=0.001\n",
    "    n_test = 1\n",
    "    if correct:\n",
    "        n_test = 400\n",
    "    significant_rdms = pos_corr_rdms[pos_corr_rdms[\"kendall_p-value\"]/2 <= (alpha/n_test)]\n",
    "    return significant_rdms\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def plot_rdm_heatmap(RDM,subject,time_window):\n",
    "    fig, ax = plt.subplots(figsize=(10,5))\n",
    "    img=ax.imshow(squareform(RDM), interpolation='nearest')\n",
    "    ax.set(title='EEG rdm of subject ' + str(subject) + ' at time window ' + str(2*(time_window[0]-100)))    # Used to return the plot as an image rray\n",
    "    ax.set_xticks(np.arange(len(stimuli)))\n",
    "    ax.set_yticks(np.arange(len(stimuli)))\n",
    "    # ... and label them with the respective list entries\n",
    "    ax.set_xticklabels(stimuli)\n",
    "    ax.set_yticklabels(stimuli)\n",
    "    for tick in ax.get_xticklabels():\n",
    "        tick.set_rotation(90)\n",
    "    fig.colorbar(img)\n",
    "    fig.canvas.draw()       # draw the canvas, cache the renderer\n",
    "    image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')\n",
    "    image  = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
    "\n",
    "    return image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "stimuli =[\"robot-drink\",\"robot-grasp\",\"robot-handwave\",\"robot-talk\",\"robot-nudge\",\n",
    "        \"robot-paper\",\"robot-turn\",\"robot-wipe\",\"android-drink\",\"android-grasp\",\"android-handwave\",\n",
    "        \"android-talk\",\"android-nudge\",\"android-paper\",\"android-turn\",\"android-wipe\",\"human-drink\",\n",
    "        \"human-grasp\",\"human-handwave\",\"human-talk\",\"human-nudge\",\"human-paper\",\"human-turn\",\"human-wipe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_indx = 104/2 + 100\n",
    "RDM_n_s = np.mean(windowed_eeg_rdm_dict_n_s[(start_indx,start_indx+1)],0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "plt.title(\"Still Mean EEG RDM at 104ms (Correlation)\",fontsize=30)\n",
    "im = plt.imshow(squareform(RDM_n_s), interpolation='nearest')\n",
    "ax.set_xticks(np.arange(len(stimuli)))\n",
    "ax.set_yticks(np.arange(len(stimuli)))\n",
    "# ... and label them with the respective list entries\n",
    "ax.set_xticklabels(stimuli)\n",
    "ax.set_yticklabels(stimuli)\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(90)\n",
    "fig.colorbar(im)\n",
    "plt.savefig(\"Still104_hamming\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "subject=0\n",
    "kwargs_write = {'fps':4, 'quantizer':'nq'}\n",
    "name = \"still_subject_\"+str(subject)+\"_EEG_RDM\"\n",
    "imageio.mimsave('./' + name +'.gif', [plot_rdm_heatmap(windowed_eeg_rdm_dict[(start_time,start_time+1)][subject]\n",
    "                                ,subject, (start_time,start_time+1))\n",
    "                                 for start_time in range(len(windowed_eeg_rdm_dict.keys()))], fps=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read pickle\n",
    "naive_still_path = '/Users/huseyinelmas/Desktop/CCN-Lab/CCN-RSA/RSA_Results/Naive/Still/correlation_hamming_avg_subjects_results.pkl'\n",
    "naive_video_path = '/Users/huseyinelmas/Desktop/CCN-Lab/CCN-RSA/RSA_Results/Naive/Video/correlation_hamming_avg_subjects_results.pkl'\n",
    "prior_still_path = '/Users/huseyinelmas/Desktop/CCN-Lab/CCN-RSA/RSA_Results/Prior/Still/correlation_hamming_avg_subjects_results.pkl'\n",
    "prior_video_path = '/Users/huseyinelmas/Desktop/CCN-Lab/CCN-RSA/RSA_Results/Prior/Video/correlation_hamming_avg_subjects_results.pkl'\n",
    "#with open(path+electrode_region+'_results.pkl', 'rb') as f: \n",
    "#    electrode_models[electrode_region], significant_rdms, eeg_rdm_dist_metric, model_rdm_dist_metric, correct, alpha, el_r = pickle.load(f)\n",
    "n_s_rdm_statistics_df, eeg_rdm_dist_metric, model_rdm_dist_metric = pd.read_pickle(naive_still_path)\n",
    "n_v_rdm_statistics_df, eeg_rdm_dist_metric, model_rdm_dist_metric = pd.read_pickle(naive_video_path)\n",
    "p_s_rdm_statistics_df, eeg_rdm_dist_metric, model_rdm_dist_metric = pd.read_pickle(prior_still_path)\n",
    "p_v_rdm_statistics_df, eeg_rdm_dist_metric, model_rdm_dist_metric = pd.read_pickle(prior_video_path)\n",
    "frames = [n_s_rdm_statistics_df, n_v_rdm_statistics_df,p_s_rdm_statistics_df,p_s_rdm_statistics_df,p_v_rdm_statistics_df]\n",
    "rdm_avg_df = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\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>experiment_type</th>\n",
       "      <th>stimulus_type</th>\n",
       "      <th>electrode_region</th>\n",
       "      <th>model_name</th>\n",
       "      <th>time</th>\n",
       "      <th>kendall_tau</th>\n",
       "      <th>kendall_p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>central</td>\n",
       "      <td>action_intention_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.025929</td>\n",
       "      <td>0.599114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12400</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.995729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4800</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>parietal</td>\n",
       "      <td>action_intention_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.037410</td>\n",
       "      <td>0.448195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5200</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>parietal</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.028647</td>\n",
       "      <td>0.561395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12000</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>action_intention_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.037020</td>\n",
       "      <td>0.452947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5600</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>parietal</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.030224</td>\n",
       "      <td>0.516584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2800</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>frontal</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.074984</td>\n",
       "      <td>0.128467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>central</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.035153</td>\n",
       "      <td>0.450607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>parietal</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.034762</td>\n",
       "      <td>0.480972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6400</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>parietal</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.044680</td>\n",
       "      <td>0.365031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14000</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.073472</td>\n",
       "      <td>0.136346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11600</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.109798</td>\n",
       "      <td>0.026015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6800</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>parietal</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.056913</td>\n",
       "      <td>0.248572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>frontal</td>\n",
       "      <td>action_intention_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.066619</td>\n",
       "      <td>0.176822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11200</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.059151</td>\n",
       "      <td>0.230454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7200</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>temporal</td>\n",
       "      <td>action_intention_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.009763</td>\n",
       "      <td>0.843108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7600</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>temporal</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.086865</td>\n",
       "      <td>0.078231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10800</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.047858</td>\n",
       "      <td>0.331924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8000</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>temporal</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.016604</td>\n",
       "      <td>0.721598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>central</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.994769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>central</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.017678</td>\n",
       "      <td>0.720047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8400</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>temporal</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.068796</td>\n",
       "      <td>0.163097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10400</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.088532</td>\n",
       "      <td>0.057443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8800</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>temporal</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.034760</td>\n",
       "      <td>0.481000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9200</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>temporal</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.087943</td>\n",
       "      <td>0.074601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.015314</td>\n",
       "      <td>0.756213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>central</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.087048</td>\n",
       "      <td>0.077604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13600</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.020065</td>\n",
       "      <td>0.684164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3200</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>frontal</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>-0.014788</td>\n",
       "      <td>0.750978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13200</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-200</td>\n",
       "      <td>0.030316</td>\n",
       "      <td>0.538820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>central</td>\n",
       "      <td>action_intention_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>0.009060</td>\n",
       "      <td>0.854273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17933</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>occipital</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.031776</td>\n",
       "      <td>0.519442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17533</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.007437</td>\n",
       "      <td>0.880148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17133</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>occipital</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.035932</td>\n",
       "      <td>0.440644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>central</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>0.132277</td>\n",
       "      <td>0.007325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16733</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>occipital</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.105875</td>\n",
       "      <td>0.031838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16333</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>occipital</td>\n",
       "      <td>action_intention_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.011715</td>\n",
       "      <td>0.812266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15933</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>gabor_video_10_frames_correlation</td>\n",
       "      <td>598</td>\n",
       "      <td>0.002424</td>\n",
       "      <td>0.952131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>central</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>0.011221</td>\n",
       "      <td>0.809713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15533</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.060046</td>\n",
       "      <td>0.223476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15133</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>flowModel_10_frames_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>0.029829</td>\n",
       "      <td>0.460124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14733</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.019170</td>\n",
       "      <td>0.697542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19133</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>occipital</td>\n",
       "      <td>gabor_video_10_frames_correlation</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.002899</td>\n",
       "      <td>0.942780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>central</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.048262</td>\n",
       "      <td>0.327856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14333</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.026193</td>\n",
       "      <td>0.595409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13933</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.035672</td>\n",
       "      <td>0.443951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13533</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>0.103367</td>\n",
       "      <td>0.036118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>central</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.016559</td>\n",
       "      <td>0.737087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13133</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>whole_brain</td>\n",
       "      <td>action_intention_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.004061</td>\n",
       "      <td>0.934381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12733</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>temporal</td>\n",
       "      <td>gabor_video_10_frames_correlation</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.020553</td>\n",
       "      <td>0.610781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12333</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>temporal</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.065267</td>\n",
       "      <td>0.185771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4733</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>frontal</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.018998</td>\n",
       "      <td>0.700127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18733</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>occipital</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.004177</td>\n",
       "      <td>0.932512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11533</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>temporal</td>\n",
       "      <td>biological_appearance_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.010816</td>\n",
       "      <td>0.826438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11133</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>temporal</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.061035</td>\n",
       "      <td>0.215941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2733</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>central</td>\n",
       "      <td>movement_nature_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.090778</td>\n",
       "      <td>0.065714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10733</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>temporal</td>\n",
       "      <td>action_target_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>0.019004</td>\n",
       "      <td>0.683403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10333</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>temporal</td>\n",
       "      <td>action_category_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.043168</td>\n",
       "      <td>0.381481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3133</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>central</td>\n",
       "      <td>gabor_video_10_frames_correlation</td>\n",
       "      <td>598</td>\n",
       "      <td>0.052701</td>\n",
       "      <td>0.191885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>prior</td>\n",
       "      <td>video</td>\n",
       "      <td>central</td>\n",
       "      <td>flowModel_10_frames_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.022872</td>\n",
       "      <td>0.571136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81600 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      experiment_type stimulus_type electrode_region  \\\n",
       "0               naive         still          central   \n",
       "12400           naive         still        occipital   \n",
       "4800            naive         still         parietal   \n",
       "5200            naive         still         parietal   \n",
       "12000           naive         still        occipital   \n",
       "5600            naive         still         parietal   \n",
       "2800            naive         still          frontal   \n",
       "800             naive         still          central   \n",
       "6000            naive         still         parietal   \n",
       "6400            naive         still         parietal   \n",
       "14000           naive         still        occipital   \n",
       "11600           naive         still      whole_brain   \n",
       "6800            naive         still         parietal   \n",
       "2400            naive         still          frontal   \n",
       "11200           naive         still      whole_brain   \n",
       "7200            naive         still         temporal   \n",
       "7600            naive         still         temporal   \n",
       "10800           naive         still      whole_brain   \n",
       "8000            naive         still         temporal   \n",
       "1200            naive         still          central   \n",
       "2000            naive         still          central   \n",
       "8400            naive         still         temporal   \n",
       "10400           naive         still      whole_brain   \n",
       "8800            naive         still         temporal   \n",
       "9200            naive         still         temporal   \n",
       "10000           naive         still      whole_brain   \n",
       "1600            naive         still          central   \n",
       "13600           naive         still        occipital   \n",
       "3200            naive         still          frontal   \n",
       "13200           naive         still        occipital   \n",
       "...               ...           ...              ...   \n",
       "333             prior         video          central   \n",
       "17933           prior         video        occipital   \n",
       "17533           prior         video        occipital   \n",
       "17133           prior         video        occipital   \n",
       "733             prior         video          central   \n",
       "16733           prior         video        occipital   \n",
       "16333           prior         video        occipital   \n",
       "15933           prior         video      whole_brain   \n",
       "1133            prior         video          central   \n",
       "15533           prior         video      whole_brain   \n",
       "15133           prior         video      whole_brain   \n",
       "14733           prior         video      whole_brain   \n",
       "19133           prior         video        occipital   \n",
       "1533            prior         video          central   \n",
       "14333           prior         video      whole_brain   \n",
       "13933           prior         video      whole_brain   \n",
       "13533           prior         video      whole_brain   \n",
       "1933            prior         video          central   \n",
       "13133           prior         video      whole_brain   \n",
       "12733           prior         video         temporal   \n",
       "12333           prior         video         temporal   \n",
       "4733            prior         video          frontal   \n",
       "18733           prior         video        occipital   \n",
       "11533           prior         video         temporal   \n",
       "11133           prior         video         temporal   \n",
       "2733            prior         video          central   \n",
       "10733           prior         video         temporal   \n",
       "10333           prior         video         temporal   \n",
       "3133            prior         video          central   \n",
       "2333            prior         video          central   \n",
       "\n",
       "                              model_name  time  kendall_tau  kendall_p-value  \n",
       "0               action_intention_hamming  -200    -0.025929         0.599114  \n",
       "12400            action_category_hamming  -200     0.000264         0.995729  \n",
       "4800            action_intention_hamming  -200     0.037410         0.448195  \n",
       "5200             action_category_hamming  -200    -0.028647         0.561395  \n",
       "12000           action_intention_hamming  -200     0.037020         0.452947  \n",
       "5600               action_target_hamming  -200     0.030224         0.516584  \n",
       "2800             action_category_hamming  -200     0.074984         0.128467  \n",
       "800                action_target_hamming  -200    -0.035153         0.450607  \n",
       "6000                       agent_hamming  -200     0.034762         0.480972  \n",
       "6400       biological_appearance_hamming  -200     0.044680         0.365031  \n",
       "14000            movement_nature_hamming  -200     0.073472         0.136346  \n",
       "11600            movement_nature_hamming  -200    -0.109798         0.026015  \n",
       "6800             movement_nature_hamming  -200     0.056913         0.248572  \n",
       "2400            action_intention_hamming  -200    -0.066619         0.176822  \n",
       "11200      biological_appearance_hamming  -200     0.059151         0.230454  \n",
       "7200            action_intention_hamming  -200    -0.009763         0.843108  \n",
       "7600             action_category_hamming  -200    -0.086865         0.078231  \n",
       "10800                      agent_hamming  -200     0.047858         0.331924  \n",
       "8000               action_target_hamming  -200     0.016604         0.721598  \n",
       "1200                       agent_hamming  -200     0.000323         0.994769  \n",
       "2000             movement_nature_hamming  -200    -0.017678         0.720047  \n",
       "8400                       agent_hamming  -200    -0.068796         0.163097  \n",
       "10400              action_target_hamming  -200     0.088532         0.057443  \n",
       "8800       biological_appearance_hamming  -200    -0.034760         0.481000  \n",
       "9200             movement_nature_hamming  -200    -0.087943         0.074601  \n",
       "10000            action_category_hamming  -200     0.015314         0.756213  \n",
       "1600       biological_appearance_hamming  -200     0.087048         0.077604  \n",
       "13600      biological_appearance_hamming  -200    -0.020065         0.684164  \n",
       "3200               action_target_hamming  -200    -0.014788         0.750978  \n",
       "13200                      agent_hamming  -200     0.030316         0.538820  \n",
       "...                                  ...   ...          ...              ...  \n",
       "333             action_intention_hamming   598     0.009060         0.854273  \n",
       "17933      biological_appearance_hamming   598    -0.031776         0.519442  \n",
       "17533                      agent_hamming   598    -0.007437         0.880148  \n",
       "17133              action_target_hamming   598    -0.035932         0.440644  \n",
       "733              action_category_hamming   598     0.132277         0.007325  \n",
       "16733            action_category_hamming   598    -0.105875         0.031838  \n",
       "16333           action_intention_hamming   598    -0.011715         0.812266  \n",
       "15933  gabor_video_10_frames_correlation   598     0.002424         0.952131  \n",
       "1133               action_target_hamming   598     0.011221         0.809713  \n",
       "15533            movement_nature_hamming   598    -0.060046         0.223476  \n",
       "15133        flowModel_10_frames_hamming   598     0.029829         0.460124  \n",
       "14733      biological_appearance_hamming   598    -0.019170         0.697542  \n",
       "19133  gabor_video_10_frames_correlation   598    -0.002899         0.942780  \n",
       "1533                       agent_hamming   598    -0.048262         0.327856  \n",
       "14333                      agent_hamming   598    -0.026193         0.595409  \n",
       "13933              action_target_hamming   598    -0.035672         0.443951  \n",
       "13533            action_category_hamming   598     0.103367         0.036118  \n",
       "1933       biological_appearance_hamming   598    -0.016559         0.737087  \n",
       "13133           action_intention_hamming   598    -0.004061         0.934381  \n",
       "12733  gabor_video_10_frames_correlation   598    -0.020553         0.610781  \n",
       "12333            movement_nature_hamming   598    -0.065267         0.185771  \n",
       "4733                       agent_hamming   598    -0.018998         0.700127  \n",
       "18733            movement_nature_hamming   598    -0.004177         0.932512  \n",
       "11533      biological_appearance_hamming   598    -0.010816         0.826438  \n",
       "11133                      agent_hamming   598    -0.061035         0.215941  \n",
       "2733             movement_nature_hamming   598    -0.090778         0.065714  \n",
       "10733              action_target_hamming   598     0.019004         0.683403  \n",
       "10333            action_category_hamming   598    -0.043168         0.381481  \n",
       "3133   gabor_video_10_frames_correlation   598     0.052701         0.191885  \n",
       "2333         flowModel_10_frames_hamming   598    -0.022872         0.571136  \n",
       "\n",
       "[81600 rows x 7 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdm_avg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>naive</td>\n",
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       "      <th>13534</th>\n",
       "      <td>naive</td>\n",
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       "      <td>-0.008973</td>\n",
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       "      <th>13545</th>\n",
       "      <td>naive</td>\n",
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       "      <td>-190</td>\n",
       "      <td>-0.024899</td>\n",
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       "      <td>naive</td>\n",
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       "      <td>-188</td>\n",
       "      <td>-0.023444</td>\n",
       "      <td>0.634580</td>\n",
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       "      <th>13567</th>\n",
       "      <td>naive</td>\n",
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       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-186</td>\n",
       "      <td>-0.018351</td>\n",
       "      <td>0.709865</td>\n",
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       "    <tr>\n",
       "      <th>13578</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-184</td>\n",
       "      <td>-0.032822</td>\n",
       "      <td>0.505791</td>\n",
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       "    <tr>\n",
       "      <th>13589</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-182</td>\n",
       "      <td>-0.024010</td>\n",
       "      <td>0.626427</td>\n",
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       "      <th>13202</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-180</td>\n",
       "      <td>-0.030396</td>\n",
       "      <td>0.537738</td>\n",
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       "      <th>13213</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
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       "      <td>-178</td>\n",
       "      <td>-0.028941</td>\n",
       "      <td>0.557380</td>\n",
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       "      <th>13224</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
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       "      <td>-176</td>\n",
       "      <td>-0.027082</td>\n",
       "      <td>0.582977</td>\n",
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       "      <th>13235</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-174</td>\n",
       "      <td>-0.012045</td>\n",
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       "      <th>13246</th>\n",
       "      <td>naive</td>\n",
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       "      <td>naive</td>\n",
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       "      <td>-170</td>\n",
       "      <td>0.034277</td>\n",
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       "      <th>13268</th>\n",
       "      <td>naive</td>\n",
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       "      <th>13279</th>\n",
       "      <td>naive</td>\n",
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       "      <th>13290</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-164</td>\n",
       "      <td>0.035974</td>\n",
       "      <td>0.465804</td>\n",
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       "      <th>13301</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-162</td>\n",
       "      <td>0.028214</td>\n",
       "      <td>0.567331</td>\n",
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       "    <tr>\n",
       "      <th>13313</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-160</td>\n",
       "      <td>0.021342</td>\n",
       "      <td>0.665249</td>\n",
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       "    <tr>\n",
       "      <th>13324</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
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       "      <td>agent_hamming</td>\n",
       "      <td>-158</td>\n",
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       "      <th>13335</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
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       "      <td>agent_hamming</td>\n",
       "      <td>-156</td>\n",
       "      <td>-0.018432</td>\n",
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       "    <tr>\n",
       "      <th>13346</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-154</td>\n",
       "      <td>0.036055</td>\n",
       "      <td>0.464802</td>\n",
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       "    <tr>\n",
       "      <th>13357</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-152</td>\n",
       "      <td>0.031205</td>\n",
       "      <td>0.526977</td>\n",
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       "    <tr>\n",
       "      <th>13368</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-150</td>\n",
       "      <td>0.009943</td>\n",
       "      <td>0.840238</td>\n",
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       "    <tr>\n",
       "      <th>13379</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-148</td>\n",
       "      <td>-0.001940</td>\n",
       "      <td>0.968624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13390</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-146</td>\n",
       "      <td>0.010348</td>\n",
       "      <td>0.833837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13401</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-144</td>\n",
       "      <td>0.044301</td>\n",
       "      <td>0.369114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13412</th>\n",
       "      <td>naive</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>-142</td>\n",
       "      <td>0.030316</td>\n",
       "      <td>0.538820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13502</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>540</td>\n",
       "      <td>-0.026031</td>\n",
       "      <td>0.597683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13503</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>542</td>\n",
       "      <td>-0.011075</td>\n",
       "      <td>0.822342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13504</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>544</td>\n",
       "      <td>-0.009297</td>\n",
       "      <td>0.850503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13505</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>546</td>\n",
       "      <td>-0.008488</td>\n",
       "      <td>0.863369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13506</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>548</td>\n",
       "      <td>-0.008812</td>\n",
       "      <td>0.858218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13507</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>550</td>\n",
       "      <td>0.021019</td>\n",
       "      <td>0.670019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13508</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>552</td>\n",
       "      <td>0.033873</td>\n",
       "      <td>0.492264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13509</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>554</td>\n",
       "      <td>0.042684</td>\n",
       "      <td>0.386844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13510</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>556</td>\n",
       "      <td>0.018998</td>\n",
       "      <td>0.700127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13511</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>558</td>\n",
       "      <td>0.019159</td>\n",
       "      <td>0.697700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13513</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>560</td>\n",
       "      <td>-0.005901</td>\n",
       "      <td>0.904767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13514</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>562</td>\n",
       "      <td>-0.048667</td>\n",
       "      <td>0.323821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13515</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>564</td>\n",
       "      <td>-0.082943</td>\n",
       "      <td>0.092658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13516</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>566</td>\n",
       "      <td>-0.065805</td>\n",
       "      <td>0.182175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13517</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>568</td>\n",
       "      <td>-0.069200</td>\n",
       "      <td>0.160639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13518</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>570</td>\n",
       "      <td>-0.060227</td>\n",
       "      <td>0.222085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13519</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>572</td>\n",
       "      <td>-0.050930</td>\n",
       "      <td>0.301826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13520</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>574</td>\n",
       "      <td>-0.042684</td>\n",
       "      <td>0.386844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13521</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>576</td>\n",
       "      <td>-0.048181</td>\n",
       "      <td>0.328667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13522</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>578</td>\n",
       "      <td>-0.031690</td>\n",
       "      <td>0.520574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13524</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>580</td>\n",
       "      <td>-0.021423</td>\n",
       "      <td>0.664059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13525</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>582</td>\n",
       "      <td>-0.022474</td>\n",
       "      <td>0.648661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13526</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>584</td>\n",
       "      <td>-0.007922</td>\n",
       "      <td>0.872397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13527</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>586</td>\n",
       "      <td>-0.030639</td>\n",
       "      <td>0.534498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13528</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>588</td>\n",
       "      <td>-0.018270</td>\n",
       "      <td>0.711086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13529</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>590</td>\n",
       "      <td>-0.025869</td>\n",
       "      <td>0.599960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13530</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>592</td>\n",
       "      <td>-0.046565</td>\n",
       "      <td>0.345157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13531</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>594</td>\n",
       "      <td>-0.053921</td>\n",
       "      <td>0.274321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13532</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>596</td>\n",
       "      <td>-0.063218</td>\n",
       "      <td>0.199968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13533</th>\n",
       "      <td>prior</td>\n",
       "      <td>still</td>\n",
       "      <td>occipital</td>\n",
       "      <td>agent_hamming</td>\n",
       "      <td>598</td>\n",
       "      <td>-0.059014</td>\n",
       "      <td>0.231533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      experiment_type stimulus_type electrode_region     model_name  time  \\\n",
       "13200           naive         still        occipital  agent_hamming  -200   \n",
       "13201           naive         still        occipital  agent_hamming  -198   \n",
       "13312           naive         still        occipital  agent_hamming  -196   \n",
       "13423           naive         still        occipital  agent_hamming  -194   \n",
       "13534           naive         still        occipital  agent_hamming  -192   \n",
       "13545           naive         still        occipital  agent_hamming  -190   \n",
       "13556           naive         still        occipital  agent_hamming  -188   \n",
       "13567           naive         still        occipital  agent_hamming  -186   \n",
       "13578           naive         still        occipital  agent_hamming  -184   \n",
       "13589           naive         still        occipital  agent_hamming  -182   \n",
       "13202           naive         still        occipital  agent_hamming  -180   \n",
       "13213           naive         still        occipital  agent_hamming  -178   \n",
       "13224           naive         still        occipital  agent_hamming  -176   \n",
       "13235           naive         still        occipital  agent_hamming  -174   \n",
       "13246           naive         still        occipital  agent_hamming  -172   \n",
       "13257           naive         still        occipital  agent_hamming  -170   \n",
       "13268           naive         still        occipital  agent_hamming  -168   \n",
       "13279           naive         still        occipital  agent_hamming  -166   \n",
       "13290           naive         still        occipital  agent_hamming  -164   \n",
       "13301           naive         still        occipital  agent_hamming  -162   \n",
       "13313           naive         still        occipital  agent_hamming  -160   \n",
       "13324           naive         still        occipital  agent_hamming  -158   \n",
       "13335           naive         still        occipital  agent_hamming  -156   \n",
       "13346           naive         still        occipital  agent_hamming  -154   \n",
       "13357           naive         still        occipital  agent_hamming  -152   \n",
       "13368           naive         still        occipital  agent_hamming  -150   \n",
       "13379           naive         still        occipital  agent_hamming  -148   \n",
       "13390           naive         still        occipital  agent_hamming  -146   \n",
       "13401           naive         still        occipital  agent_hamming  -144   \n",
       "13412           naive         still        occipital  agent_hamming  -142   \n",
       "...               ...           ...              ...            ...   ...   \n",
       "13502           prior         still        occipital  agent_hamming   540   \n",
       "13503           prior         still        occipital  agent_hamming   542   \n",
       "13504           prior         still        occipital  agent_hamming   544   \n",
       "13505           prior         still        occipital  agent_hamming   546   \n",
       "13506           prior         still        occipital  agent_hamming   548   \n",
       "13507           prior         still        occipital  agent_hamming   550   \n",
       "13508           prior         still        occipital  agent_hamming   552   \n",
       "13509           prior         still        occipital  agent_hamming   554   \n",
       "13510           prior         still        occipital  agent_hamming   556   \n",
       "13511           prior         still        occipital  agent_hamming   558   \n",
       "13513           prior         still        occipital  agent_hamming   560   \n",
       "13514           prior         still        occipital  agent_hamming   562   \n",
       "13515           prior         still        occipital  agent_hamming   564   \n",
       "13516           prior         still        occipital  agent_hamming   566   \n",
       "13517           prior         still        occipital  agent_hamming   568   \n",
       "13518           prior         still        occipital  agent_hamming   570   \n",
       "13519           prior         still        occipital  agent_hamming   572   \n",
       "13520           prior         still        occipital  agent_hamming   574   \n",
       "13521           prior         still        occipital  agent_hamming   576   \n",
       "13522           prior         still        occipital  agent_hamming   578   \n",
       "13524           prior         still        occipital  agent_hamming   580   \n",
       "13525           prior         still        occipital  agent_hamming   582   \n",
       "13526           prior         still        occipital  agent_hamming   584   \n",
       "13527           prior         still        occipital  agent_hamming   586   \n",
       "13528           prior         still        occipital  agent_hamming   588   \n",
       "13529           prior         still        occipital  agent_hamming   590   \n",
       "13530           prior         still        occipital  agent_hamming   592   \n",
       "13531           prior         still        occipital  agent_hamming   594   \n",
       "13532           prior         still        occipital  agent_hamming   596   \n",
       "13533           prior         still        occipital  agent_hamming   598   \n",
       "\n",
       "       kendall_tau  kendall_p-value  \n",
       "13200     0.030316         0.538820  \n",
       "13201     0.005416         0.912561  \n",
       "13312     0.029669         0.547515  \n",
       "13423     0.028456         0.564005  \n",
       "13534    -0.008973         0.855645  \n",
       "13545    -0.024899         0.613706  \n",
       "13556    -0.023444         0.634580  \n",
       "13567    -0.018351         0.709865  \n",
       "13578    -0.032822         0.505791  \n",
       "13589    -0.024010         0.626427  \n",
       "13202    -0.030396         0.537738  \n",
       "13213    -0.028941         0.557380  \n",
       "13224    -0.027082         0.582977  \n",
       "13235    -0.012045         0.807075  \n",
       "13246     0.021180         0.667632  \n",
       "13257     0.034277         0.487114  \n",
       "13268     0.026435         0.592007  \n",
       "13279     0.022959         0.641605  \n",
       "13290     0.035974         0.465804  \n",
       "13301     0.028214         0.567331  \n",
       "13313     0.021342         0.665249  \n",
       "13324     0.021665         0.660492  \n",
       "13335    -0.018432         0.708645  \n",
       "13346     0.036055         0.464802  \n",
       "13357     0.031205         0.526977  \n",
       "13368     0.009943         0.840238  \n",
       "13379    -0.001940         0.968624  \n",
       "13390     0.010348         0.833837  \n",
       "13401     0.044301         0.369114  \n",
       "13412     0.030316         0.538820  \n",
       "...            ...              ...  \n",
       "13502    -0.026031         0.597683  \n",
       "13503    -0.011075         0.822342  \n",
       "13504    -0.009297         0.850503  \n",
       "13505    -0.008488         0.863369  \n",
       "13506    -0.008812         0.858218  \n",
       "13507     0.021019         0.670019  \n",
       "13508     0.033873         0.492264  \n",
       "13509     0.042684         0.386844  \n",
       "13510     0.018998         0.700127  \n",
       "13511     0.019159         0.697700  \n",
       "13513    -0.005901         0.904767  \n",
       "13514    -0.048667         0.323821  \n",
       "13515    -0.082943         0.092658  \n",
       "13516    -0.065805         0.182175  \n",
       "13517    -0.069200         0.160639  \n",
       "13518    -0.060227         0.222085  \n",
       "13519    -0.050930         0.301826  \n",
       "13520    -0.042684         0.386844  \n",
       "13521    -0.048181         0.328667  \n",
       "13522    -0.031690         0.520574  \n",
       "13524    -0.021423         0.664059  \n",
       "13525    -0.022474         0.648661  \n",
       "13526    -0.007922         0.872397  \n",
       "13527    -0.030639         0.534498  \n",
       "13528    -0.018270         0.711086  \n",
       "13529    -0.025869         0.599960  \n",
       "13530    -0.046565         0.345157  \n",
       "13531    -0.053921         0.274321  \n",
       "13532    -0.063218         0.199968  \n",
       "13533    -0.059014         0.231533  \n",
       "\n",
       "[1200 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdm_avg_df[(rdm_avg_df.electrode_region == \"occipital\") &\n",
    "           (rdm_avg_df.model_name==\"agent_hamming\") & (rdm_avg_df.stimulus_type==\"still\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[66, 264]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_times_ns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[102, 298]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_nv_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plot_RDM_comparison(windowed_eeg_rdm_dict_n_v,windowed_eeg_rdm_dict_n_s,rdm_avg_df,model=\"\",start_time=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([66, 258], [158, 300])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrode_region='occipital'\n",
    "model_name='agent_hamming'\n",
    "\n",
    "significant_rdms = find_significant_rdms(rdm_avg_df[rdm_avg_df.stimulus_type==\"still\"])\n",
    "df = significant_rdms[(significant_rdms.electrode_region == electrode_region) \n",
    "                      & (significant_rdms.model_name == model_name) ]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  }
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