04 - CCA + SSVEP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Canonical correlation analysis on SSVEP dataset\n",
    "\n",
    "Welcome to the fourth part.  In this part, we shall try canonical correlation analysis on SSVEP.  SSVEP is a very simple neuro paradigm where the brain frequency will resemble to the frequency of the attended stimuli.  For example, if I am looking at a circle flicking at 6Hz, my brain signal will oscillate at 6Hz as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import mne\n",
    "from mne import create_info\n",
    "from mne.io import RawArray\n",
    "from mne.time_frequency import psd_welch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/ssvep-20trials-3s-chaky-bigsquare.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62436, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #check the size of 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's rename our channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'Unnamed: 1':'O1','Unnamed: 2':'Oz', 'Unnamed: 3':'O2'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>4</th>\n",
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       "   timestamps         O1         Oz         O2  Unnamed: 4  Unnamed: 5  \\\n",
       "0   62389.150  38956.250  27233.611  44154.887    187500.0 -187500.016   \n",
       "1   62389.150  39004.645  27278.182  44197.668    187500.0 -187500.016   \n",
       "2   62389.151  39005.805  27278.383  44199.301    187500.0 -187500.016   \n",
       "3   62389.152  38978.270  27251.068  44177.395    187500.0 -187500.016   \n",
       "4   62389.152  38944.137  27219.082  44149.145    187500.0 -187500.016   \n",
       "\n",
       "   Unnamed: 6  Unnamed: 7  Unnamed: 8  Marker  \n",
       "0 -187500.016 -187500.016 -187500.016       0  \n",
       "1 -187500.016 -187500.016 -187500.016       0  \n",
       "2 -187500.016 -187500.016 -187500.016       0  \n",
       "3 -187500.016 -187500.016 -187500.016       0  \n",
       "4 -187500.016 -187500.016 -187500.016       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop the channel we did not use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"timestamps\", \"Unnamed: 4\", \"Unnamed: 5\", \"Unnamed: 6\", \"Unnamed: 7\", \"Unnamed: 8\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>Oz</th>\n",
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       "      <th>Marker</th>\n",
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       "          O1         Oz         O2  Marker\n",
       "0  38956.250  27233.611  44154.887       0\n",
       "1  39004.645  27278.182  44197.668       0\n",
       "2  39005.805  27278.383  44199.301       0\n",
       "3  38978.270  27251.068  44177.395       0\n",
       "4  38944.137  27219.082  44149.145       0"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So in our dataset, we got three targets flashing at 6Hz, 10Hz, and 15Hz.  Each target happens 10 times.  They are represented by label of 1, 2, 3 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 4)\n",
      "(20, 4)\n",
      "(20, 4)\n"
     ]
    }
   ],
   "source": [
    "print(df[df.Marker==2].shape) #10hz\n",
    "print(df[df.Marker==1].shape) #6hz\n",
    "print(df[df.Marker==3].shape) #15hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <th>39785</th>\n",
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       "      <td>50363.176</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>42820</th>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>45842</th>\n",
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       "      <td>50732.449</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>48863</th>\n",
       "      <td>43674.660</td>\n",
       "      <td>32731.225</td>\n",
       "      <td>51191.441</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>51885</th>\n",
       "      <td>43852.691</td>\n",
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       "      <td>51433.688</td>\n",
       "      <td>3</td>\n",
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       "      <th>54906</th>\n",
       "      <td>43968.184</td>\n",
       "      <td>32739.271</td>\n",
       "      <td>51408.988</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>57929</th>\n",
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       "      <td>3</td>\n",
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       "      <th>60962</th>\n",
       "      <td>44397.516</td>\n",
       "      <td>33343.172</td>\n",
       "      <td>51907.254</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "              O1         Oz         O2  Marker\n",
       "3502   39410.773  27877.766  45026.629       3\n",
       "6523   39726.336  28277.191  45611.887       3\n",
       "9544   40148.473  28786.275  46270.840       3\n",
       "12580  40494.566  29369.209  46862.020       3\n",
       "15601  40471.680  29290.598  47066.648       3\n",
       "18623  41030.492  29709.939  47910.137       3\n",
       "21644  41887.215  30636.396  48849.848       3\n",
       "24665  41506.543  30342.336  48827.363       3\n",
       "27700  41632.316  30459.996  49022.582       3\n",
       "30721  41729.230  30484.383  49195.586       3\n",
       "33742  42148.773  31121.631  49834.914       3\n",
       "36764  42540.934  31580.914  50115.496       3\n",
       "39785  42526.227  31597.074  50363.176       3\n",
       "42820  42519.879  31379.928  50146.383       3\n",
       "45842  43143.023  32423.709  50732.449       3\n",
       "48863  43674.660  32731.225  51191.441       3\n",
       "51885  43852.691  32890.504  51433.688       3\n",
       "54906  43968.184  32739.271  51408.988       3\n",
       "57929  45557.281  34772.027  53030.723       3\n",
       "60962  44397.516  33343.172  51907.254       3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Marker==3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Transform Data into Raw MNE object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_raw(df):\n",
    "    sfreq = 125  #our OpenBCI headset sampling rate\n",
    "    ch_names = list(df.columns)\n",
    "    ch_types = ['eeg'] * (len(df.columns) - 1) + ['stim']\n",
    "    ten_twenty_montage = mne.channels.make_standard_montage('standard_1020')\n",
    "\n",
    "    df = df.T  #mne looks at the tranpose() format\n",
    "    df[:-1] *= 1e-6  #convert from uVolts to Volts (mne assumes Volts data)\n",
    "\n",
    "    info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=sfreq)\n",
    "\n",
    "    raw = mne.io.RawArray(df, info)\n",
    "    raw.set_montage(ten_twenty_montage)   \n",
    "\n",
    "    return raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=4, n_times=62436\n",
      "    Range : 0 ... 62435 =      0.000 ...   499.480 secs\n",
      "Ready.\n"
     ]
    }
   ],
   "source": [
    "raw = df_to_raw(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective window size : 2.048 (s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chaklam/DSAI/Environments/teaching_env/lib/python3.8/site-packages/mne/viz/utils.py:137: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  (fig or plt).show(**kwargs)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<MNELineFigure size 720x252 with 2 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " raw.compute_psd().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notch Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Power-line noise is a noise created by the electrical network. It is composed of sharp peaks at 50Hz (or 60Hz depending on your geographical location). Some peaks may also be present at the harmonic frequencies, i.e. the integer multiples of the power-line frequency, e.g. 100Hz, 150Hz, … (or 120Hz, 180Hz, …).\n",
    "\n",
    "Remove the 50Hz power line noise in Thailand.  We will also be remove its harmonics, i.e., 100Hz, 150Hz, etc.  Since our signal is 125Hz (250Hz / 2 according to Nyquist Theorem), we shall run the harmonics until 125 Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up band-stop filter\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandstop filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower transition bandwidth: 0.50 Hz\n",
      "- Upper transition bandwidth: 0.50 Hz\n",
      "- Filter length: 825 samples (6.600 sec)\n",
      "\n",
      "Effective window size : 2.048 (s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s finished\n",
      "/Users/chaklam/DSAI/Environments/teaching_env/lib/python3.8/site-packages/mne/viz/utils.py:137: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  (fig or plt).show(**kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ToN5jVC7dLCIg8TmIC2Tese3FMo52saXqvDbYcT9mZFk+7rJs3Gb5uM3ygYMHD1JSUsLEiRPZt28f6si0WwPNpra2lrKyMkpKSjh4cGj/3J+SDeKj2qhjnMw+9nwMY+jQDg8rMsYYY4zxxuHDh8nPz2fs2LH4/X4OHDjgdUkDFolE2LVrF7NnzyY/P59Dhw4N6flTskEssTT86dCuQYopOb796N2UbtxUOWr1XDjFuMfycZdl4zbLx22WD5x//vk8++yzRKNRiouLefrpp70uCRhYNq+//jq5ublkZWXx5JNPcsEFFwxpDc40iEUkU0TeEJFNIrJNRO5IbJ8uIq+LyC4R+a2I9HtbYU5sHHml8TmIfXLiJQoC2OIcXiooKPC6BJOE5eMuy8Ztlo/bLJ/4am+zZs1i7ty5tLW1sWbNGq9LAgaWzYsvvkhaWhpz5szhyJEjvOMd7xjSGgJDerbTEwYuV9WgiKQBa0TkCeDzwP+o6kMi8lPgY8BPkp0ovauY3D7mX+4mg8wxYTqPZJJVMKT1mwE6cOAA+fn5Xpdh+mD5uMuycZvl4zbLJ+6ZZ57h9ddfR1W55ZZbUFXP5yMeSDbPPPMM3//+9xk/fjznnnsufr9/SGtwpodY447OoZGWeChwOfD7xPb7gPf0d65cShNzEL+1M7mTPLKLm23qNQ9NmTLF6xJMEpaPuywbt1k+brN84tLS0rj44ou5+OKLAVi7dq3HFfWfTW1tLRs3bmTVqlVceumlZGVlDXkNzjSIAUTELyIbgUbgGaAKaFHVSOKQWmBif+fJ1HzIraGbty5L6GMcvpJaW5zDQzt37vS6BJOE5eMuy8Ztlo/bLJ8TiQgf+9jH+PGPf+x1Kf1m87Of/YwbbrjhtGYKWbZsWdL9TjWIVTWqqouBScAKYF7yVxwnIreKyDoRWRfqDNFwZANZTdOpq6ujubmZqqoqQqEQaTvLiI7dyZbXqoDj89Jt2LCBWCxGeXk5oVCIqqoqmpubqauro6GhgaamJqqrqwkGg1RUVBCJRNi0adMJ5zj6dcuWLYTDYSorK2ltbaWmpobGxkYaGxupqamhtbWVyspKwuHwsfW7Tz7Hpk2biEQiVFRUEAwGqa6upqmpiYaGhrdcU3l5ObFYjA0bNpwR15SWlpZy15RKOcVisZS7plTJ6Zxzzkm5a0qlnHrmkyrXlEo5zZkzJ+Wu6XRzevvb385jjz3Gs88+6+k1jRkzps9ram1t5Uc/+hGf/exnTyun/hrd4socdCcTkduBEPBlYIKqRkTkAuBrqpp0JPW1Zf+qP6gdQ4RLOMt34l2IW2MbydJHaf317Sy5abiqN8msX7++39/UjHcsH3dZNm6zfNxm+fTuX//1X6mtreW+++7zrIZk2fzbv/0be/fu5f777z/t9xGR9aq6vLd9zvQQi0iJiBQkvs8CrgS2Ay8A708cdiPwp/7PBUFqT5iD+KhimUC7NMdHJxtP2A8kt1k+7rJs3Gb5uM3y6d2Xv/xlnn32WU9nnOgrm8rKSn7yk5/wn//5n6f9Hjt27Ei635kGMVAKvCAim4G1wDOq+mfiPcSfF5FdwFjgnv5OtE9fpV3bKeplDHExxQS1fWgrN4PS3/KJxluWj7ssG7dZPm6zfHqXn5/PXXfdxc0330xbW9uAX/dY9DHu6P46e2N7j21ra4BX/wf2vNDzyG7gu8C/AL0vF91bNt3d3dx4443cfvvtTJo0acB19aWhoSHpfmeHTJyOuXPn6t3lF3GJ/5e97l8T/Rg599/DkptHuDBjjDHGGAd9/OMfp62tjYceeqjfadhWR1fzzq5VdNJJKaXsy9wLCN+fDsEGED/c+BxMvgDga8B3gCjxP/4/PqB6vvCFL1BeXs7jjz+Oz3f6/berV69m5cqV7g+ZGErhcDjpfkVBsMU5PHJ0wL1xk+XjLsvGbZaP2yyf5H74wx9SXV3NV7/61X6P3avxXuEYMQ5wAEXRGLTug2hXfOhq07ERCvVAF/Ge4vpez3dyNj/96U959NFHeeCBB4akMTwQKdkgzshI63UO4qNipJGR101XsM9DzDA6++yzvS7BJGH5uMuycZvl4zbLJ7msrCwee+wxHn74Yb75zW+SbATBB/wf4ALf+RRRxA8DP8AnPnx+uOJbEMiCcQtg/vuOHn07sByYA/y81/P1zObee+/lG9/4Bk888QRjx7516OtwSckGcSQaIsa4Pvd3kkt2SYvNReyRXbt2eV2CScLycZdl4zbLx22WT//GjRvHCy+8wIMPPshnP/tZIpFIr8dlSzbPZjzDoayDfDLtk8e2X/xP8NUO+MTrkJF3dOsk4HVgB3Bur+fbtWsXqsq3vvUtbr/9dp599llmzZo1lJfWr5RsEPsCMXLp+x8yooVkFO+31eo8MhSD483wsXzcZdm4zfJxm+UzMKWlpaxZs4aKigquuuoq6urqhv09c3Jy+OAHP8jDDz/Mq6++yrx5A16GYsikZINYpJsC6fsfM1umEi3ZZT3EHmlqavK6BJOE5eMuy8Ztlo/bLJ+BKygo4IknnuCyyy5jyZIl/OIXvyAajQ75+6gqv//971mxYgUTJkxgzZo1TJzY74LEpyQvLy/p/pRsEAcypdc5iI/KlxlEC3cSOjyCRZljcnNzvS7BJGH5uMuycZvl4zbLZ3D8fj+33347Tz31FPfeey9Lly7lkUceGZKGsary3HPPcemll3LHHXfw05/+lB/+8IentTRzf86opZuHSgxlDAV97i9mEh2+Q+jQ/7JjBqC7u9vrEkwSlo+7LBu3WT5us3xOzZIlS1izZg1f//rX+da3vsWcOXP4xje+wY4dO5LeeNebmpoa/vu//5uFCxdy2223ceutt/Lmm2+yYsWKYap+4AJeFzAcREk6h16xFFOuwdS8+DNALBbzugSThOXjLsvGbZaP2yyfUycivPvd7+a6665j7dq13H///VxxxRWkp6dz0UUXsWjRImbNmsWECRPIyclBRGhvb6exsZGqqiq2bNnCK6+8QlNTE6tWreIHP/gBK1euPNZWcyGblGwTdnV1Jd2fTTad2on98cQb2dnZXpdgkrB83GXZuM3ycZvlc/pEhBUrVrBixQp++MMfUl5ezquvvsq2bdt48cUX2b9/Px0dHagq2dnZjBs3junTp7Ns2TI+85nPsGjRIvx+/1vOOxLZrF69Oun+lGwQx6Jv/cfuqb8VWMzwOnz4MIWFhV6XYfpg+bjLsnGb5eM2y2doiQhnn332kMzv7EI2KTmGOBLL6fcYxUcgK0p3aAQKMicoKyvzugSThOXjLsvGbZaP2ywfd7mQTUo2iAOR/ju+w+SRU9JKh83CMuL27NnjdQkmCcvHXZaN2ywft1k+7nIhm5RsEKen971s81FhzSNr7CFrEHvAiwm3zcBZPu6ybNxm+bjN8nGXC9mkZIO4s7Oz32PSZSJSsscW5/DAxo0bvS7BJGH5uMuycZvl4zbLx10uZJOSDeKBTOycy3QiRTuth9gDS5cu9boEk4Tl4y7Lxm2Wj9ssH3e5kM2gG8QikiMiyadx8NhAJoouksl0ZtQTDY9AQeYE69ev97oEk4Tl4y7Lxm2Wj9ssH3eNRDZz5sxJul/6azyKiA+4HvgwcC4QBjKAJuBx4Gequmsoih0qy5cv13Xr1iU95pAeokbvgPvuZMnNI1SYMcYYY4zxhIisV9Xlve0bSA/xC8BM4CvABFWdrKrjgIuB14DviMhHhqzaIdDR0dHvMYUU0qH9H2eG3oYNG7wuwSRh+bjLsnGb5eM2y8ddLmQzkB7iNFVNugD4QI4ZSQsXLtTNmzf3e9xL0VvIvf8eltxsC3WMpFgshs+XksPXU4Ll4y7Lxm2Wj9ssH3eNRDb19fVMnDjx1HuIe2voikix9FjuzaXGMEBbW9uAjusiF1+gnWhkmAsyJ6ioqPC6BJOE5eMuy8Ztlo/bLB93jUQ2O3fuTLq/3waxiJwvIqtF5A8iskREtgJbgQMicvUQ1Tmk0tLSBnRchxaQVdRE6PAwF2ROMH36dK9LMElYPu6ybNxm+bjN8nGXC9kMpH/6LuA/gQeB54GPq+oE4FLgW8NY2ymLRAbW5dtFMRnFdXTYXMQjqr6+3usSTBKWj7ssG7dZPm6zfNzlQjYDaRAHVPVpVf0/YL+qvgagqkPavy0ik0XkBREpF5FtIvLZxPYiEXlGRCoTXwv7O5ffP7BZ4cbIHGLFO2wu4hFWVFTkdQkmCcvHXZaN2ywft1k+7nIhm4E0iGM9vg+dtK//CX8HLgJ8QVXnA+cDnxGR+cA/A8+p6mzgucTzpGKxWH+HADBBJtFdXG+r1Y2wgcwCYrxj+bjLsnGb5eM2y8ddLmQTGMAxi0SkFRAgS0TaiDeEBeh/SbgBUtUGoCHxfZuIbAcmAu8GLkscdh+wGvjyULxnqZSyN7cRGdg9eGaI2F2+brN83GXZuM3ycZvl4y4XshnILBN+Vc1X1TxVDSS+Hn0+sLvXBklEpgFLgNeB8YnGMsB+YHwfr7lVRNaJyLojR47Q1NREQ0MDdXV1NDc3U1VVRSgUory8nFgsxoYNGyiiiEhNfIKMDRs2EIvFKC8vJxQKUVVVRXNzM3V1dTQ0NNDU1ER1dTXBYJCKigoikQibNm0Cjq+wcvTrli1bCIfDVFZW0traSk1NDY2NjTQ2NlJTU0NrayuVlZWEw2G2bNnS6zk2bdpEJBKhoqKCYDBIdXX1gK6p5zlcvaaGhoaUu6ZUyqmmpiblrilVckpLS0u5a0qlnHrmkyrXlEo5HT02la4pVXJqbm4e9mvasWMHyQxkHuLPJ9uvqv+d9ASDJCK5wF+Bb6rqH0SkRVULeuxvVtWk44gHOg8xwOropxhz//dZcnPG6ZRtBqG6uppp06Z5XYbpg+XjLsvGbZaP2ywfd41UNslWqhvIkIm8xNe5xJdufjTxfBXwxumXd5yIpAEPA/+rqn9IbD4gIqWq2iAipUBjf+cJBAZyWXEdWsAYWuij49kMg+LiYq9LMElYPu6ybNxm+bjN8nGXC9kMZMjEHap6BzAJWKqqX1DVLwDLgClDVUhioY97gO0n9To/CtyY+P5G4E/9naurq2vA79tOAUgz/XSUmyFUW1vrdQkmCcvHXZaN2ywft1k+7nIhm8GMYh4P9GxpdjG03aoXAR8FLheRjYnHtcC3gStFpBJ4e+J5UtFodMBvKkzEl1dPuPUUqzaDNmvWLK9LMElYPu6ybNxm+bjN8nHXSGRzdBxyXwbTIL4feENEviYiXyN+w9u9p1zZSVR1jaqKqi5U1cWJx19U9ZCqXqGqs1X17ara77pyodDJs8P1bbxMJWZTr42obdu2eV2CScLycZdl4zbLx22Wj7tGIpu2tuRTivV7U90JB4ssBS5JPH1RVd889dKGz9y5c7W/uwmP2hXbRXPFo0xo/TyTzx/mwowxxhhjzIhbvXo1K1eu7POmun57iBNjewFQ1Q2q+oPE483ejnFBZ2fngI8tlVLCY+tt+eYR1N+fLYy3LB93WTZus3zcZvm4y4VsBjJk4gURuU1ETriBTkTSReRyEbmP4ze9OSEzc+DrheRIDpGiNkLNkWGsyPS0bNkyr0swSVg+7rJs3Gb5uM3ycZcL2QykQXw1EAUeFJF6ESkXkT1AJXAD8H1VvXcYaxy0wfQQA3T68tCo3VU3Ulz4TdD0zfJxl2XjNsvHbZaPu1zIpt8Je1W1E/gx8OPEPMHFQEhVW4a5tlM2mB5igA4tBJqBomGpx5zIhd8ETd8sH3dZNm6zfNxm+bjLhWwGtXi0qnaraoPLjWEY3LRrAB0UoNo8TNWYkx1dktG4yfJxl2XjNsvHbZaPu0Yim9LS0qT7B9UgPlOMGTNmUMfnyUTCtAxPMeYt5syZ43UJJgnLx12WjdssH7dZPu4aiWzmzp2bdH9KNogHs1IdQJlMpDuzle6BT19sTkNNTY3XJZgkLB93WTZus3zcZvm4y4VsUrJB7PMN7rJKpZRI8UE6moapIHOC8eOHcoFDM9QsH3dZNm6zfNxm+bhrJLLpb2GOAbccReS5xFLKPbf9/BTrGlbBYHBQx49nPN3FjXQ0DXyREnPqWlpavC7BJGH5uMuycZvl4zbLx10jkc1QLt08HfiyiPx7j229rvbhtcGuE5ImaXQXR2g/eGSYKjI9DXYWEDOyLB93WTZus3zcZvm4y4VsBtMgbgGuAMaLyGMiMrg71xzXPTZAR1OL12UYY4wxxpgRNpgGsahqRFU/DTwMrAHGDU9Zp0d18EMfOjPyiYath3gkDHbhFDOyLB93WTZus3zcZvm4y4Vs+l2Yo4efHv1GVe8Vkc3APwx9SadvsDfVAXQzhpjaanUjoaCgwOsSTBKWj7ssG7dZPm6zfNzlQjb9NohF5IeAJr6/86Tdg7t7bYQMdmEOgAlSRoiOYajGnOzAgQPk5+d7XYbpg+XjLsvGbZaP2ywfd7mQzUB6iNf1+P4O4N/7OtAVgcBgOr7jJsgEDmjVMFRjTjZlyhSvSzBJWD7usmzcZvm4zfJxlwvZ9Du2QFXvO/oAmns+T2xzjt/vH/RrSqWUkL+DaMSmXhtuO3fu9LoEk4Tl4y7Lxm2Wj9ssH3eNRDbLli1Lun+wg23PiNZiTk7OoF9TTDHholZCh5NP3GxO3znnnON1CSYJy8ddlo3bLB+3WT7uGols8vLyku5PyZXqOjoGPxZYRIgWQ8fBg8NQkempv8mxjbcsH3dZNm6zfNxm+bjLhWykvynKRKSNeM+wAFlw7M4zAVRVnRuhvmDBAt26deugX/fAzl9wccNSpr0tebe6McYYY4w5c+zYsYN58+atV9VeF5UbSA/xO4ACVc1T1YCq5iceeS42hqH/9ar70lWSRvvB9iGuxpzMhd8ETd8sH3dZNm6zfNxm+bhrJLJpaGhIun8g0zF8FLhLRHYCTwJPqur+IajtLUTkl8C7gEZVXZDYVgT8FpgGVAMfVNXmZOc51SUAs3IzCbW2Ee0+SHB/Ca110FYH4Vbi/eE9dHfAvPdAftkpvdWo1t/AduMty8ddlo3bLB+3WT7uciGbgcwy8SlVXQp8DSgE7hWRV0XkP0XkUhEZ/JQOfbsXuPqkbf8MPKeqs4HnEs+TCofDp/TmZb5SjqQtYutDuzi08zkyCzqZeRUsuRmW3HTiY9mtUPkXaHjzlN5qVNu0aZPXJZgkLB93WTZus3zcZvm4y4Vs+h1D3OuLRLKAlcA1wAV9jcc4pYJEpgF/7tFDvAO4TFUbRKQUWK2qc5OdY+7cubpjx45Bv/fO2E4O6kEu8l8ENAJ/BKYAV9Hb7w6qsPNxEB/MuXbQbzdqRSKRU5or2owMy8ddlo3bLB+3WT7uGolsVq9ezcqVK099DLGIzBCRZYnvLwFQ1ZCq/kVVbxvKxnAfxqvq0YEf+4Hx/b2gq6vrlN6oVEpp4OhbjQNuBaYDdwO/BP4EbAfiPdAiMPddkDse3vwVRCOn9Lajzq5du7wuwSRh+bjLsnGb5eM2y8ddLmQzkJvqfgJ8QET+jvh4Ys9ovDu71y5tEblVRNaJyLq2tjaamppoaGigrq6O5uZmqqqqCIVClJeXE4vF2LBhA3B8IPeGDRvIjmXTvb2bUChEVVUVzc3N1NXl0tCwiqamVdTUTKGjo576+juJRr/P1q0vANDAemZcCX/4532U/wFefmQn7a1hKisraW1tpaamhsbGRhobG6mpqaG1tZXKykrC4TBbtmw5oY6jXzdt2kQkEqGiooJgMEh1dfUpXVMsFqO8vPyka6qjoaGBpqYmqqurCQaDVFRUEIlEjv3Z4uR6tmzZQjg8NNfU1dWVcteUSjkFg8GUu6ZUyWnSpEkpd02plFPPfFLlmlIpp3HjxqXcNaVKTn6/f9ivqb+RAwOZdu2HqnqbiHwOuEVVFyZ9wWkaiiETs2fP1srKylN6/xeiL1ChFSyWxZzvOx8R6ePILuAXwLuBSce2hlrg0A44vAsi4Xgv8lnvhcwxp1ROSqqurmbatGlel2H6YPm4y7Jxm+XjNsvHXSORzfr161m+fHmfQyYGMmDjcQBV/b6IeDEo4FHgRuDbia9/6u8Fp7JS3VEr/Su5TC9jk27i59GfM0EmcKXvSrIl+6Qj04FPEh9O8XZgJgBZBTDpvPgDoKsD3vwlzLoaxs465bJSSm5urtclmCQsH3dZNm6zfNxm+bhrJLLpbyaLgTSIV4lIm6q+rKp3DU1ZvRORB4HLgGIRqQX+nXhD+Hci8jFgL/DB/s5zKjcKnlQHi2Uxi32Lqdd6Ho4+TBddZJDBWb6zOFvOJlMyAT/xccb3AiFgwVvOlZ4NKz4DW38LrbUw/bKB1dCyF6qeBgQKpkHpUsguOq3LckZ3d7fXJZgkLB93WTZus3zcZvm4y4VsBtIg3gl8LzFc4XfAg6o6LJONqeoNfey6YjjebyDKpIyPBuJDp8MaplzLeTj6MEGCTJfprPStJE1uIj5V8hFgKhBMPNqBCCIrOed6HzUvw6YH4Jy/BV8fo7db9sKuJyFvIiy+CXwBOFIDe/8KocPxGS3Sc0H84PMf/1q6DHLHDf+/x1CIxWJel2CSsHzcZdm4zfJxm+XjLhey6bdBrKo/AH4gIlOB64FfJqZde5B443jnMNc4aB0dHf0fdAoyJIMlsoQlviUA7Irt4r7ofeSSy9X+qymQ7UAFkJt4jCXeKL4H+DhTLhLyJ8Hrd0LuBEjLPv7wBWD/m5BbCktuAX/a8fctmBp/AMRiEOkEjUIsGv8a7T7MrifzyBmXxqx3xMctuyw7++ThJ8Yllo+7LBu3WT5us3zcNRLZrF69Oun+AU/6pqp7ge8A3xGRJcTnIbud+LgBp0Sj0RF5n1m+WczyzaJZm3ky+iRttJFJJoVSyBi6KRQokRLGy+XE/7luoWCqcP5n443a7g7oDsW/Rjph8c0QSO/5DpXE50POA/KBPHy+PNKzDwCbgQbiS+gVsfjv6ti/+Ure+NFczrkBsseOyD/BKTl8+DCFhYVel2H6YPm4y7Jxm+XjNsvHXS5kM+AGsYgEiC/EcT3xIQyria9e55yRnni7UAq5PnA9ABGNcIQjNGszLbTwbPRZpspULvZfAtwH3IiIkJYFaVl9nXE/qo/wSqyB/ZrFWElnshRQJrlkSQQoAZYCEzi+prQyYeHzjJ3zGlsfvJ4xUzKYfrmbvcVlZbbetcssH3dZNm6zfNxm+bjLhWz6bTmKyJXADcA7gdeBh4BbVbV9mGs7ZV4Ozg5IgLGMZazEu2iX+5bzu8jv2BYr5GzfCuAB4CMcb8j21A48Qkx93B/1ca7vQ1zom08zzezW3WyI7SGoXQj1QD0AmpiWOUaMG/w3kJ3ZxZKb76V+/YVs+905LPjQCFz0IO3Zs4f58+d7XYbpg+XjLsvGbZaP2ywfd7mQzUDmIX4e+A3wsKo2n7RvuqruGcb6TsmpLt08XFSVe6L3cLX/aiZJM/AmsJD4jXdtPb7G6NZ38YvoI1zru4ZpvmkDfo9mbeah6EMs8y1jhW8F8BK7n/cTyLyQKRcO/TWdjlgshq+vuwqN5ywfd1k2brN83Gb5uGsksjntpZtV9XJVvRu4TURu7/G4A3h+qAseCp2dnV6XcAIR4Wb/zTwS/SMtOhl4W2LPBOJDH64FbqZD/5afRP+P9/vfN6jGMMSHbXwq8CnCGuYXkV/QpouZcXkth3c1cWTfkF7Oadu4caPXJZgkLB93WTZus3zcZvm4y4Vs+u0hPnagyBd6PE0DLgW2qOqXh6Ow0+FaD/FRIQ3xs+jP+aT/7xPzGB93SA/x6+gD3Oy/iTFyesvatWorD0Yf5BLfhczTF3jjrs+w9BN+0u0GW2OMMcaMQv31EA+4QfyWF4r4gDf6OrGX5syZozt3OjcbHACH9TD3Re+nmLFEOL7wXw45rPKvIkv6vNNuUFSVu6N3c51/GWNad7Hp1x9kxWfcuMlu/fr1/a4YY7xj+bjLsnGb5eM2y8ddI5FNfX09EydOHJYG8VjgflV95+kUOByWL1+u69at87qMPoU0RBppBGR4Z8OIaIQfR3/Cx/2lhHYtomHDbM9usouEywlkzGEQE5sYY4wxxgwZETn9BrGIbAGOHizANOAQ8bvBVFUXnn6pQ2P+/PlaXl7udRlOaNM2fhn9Jf/gh70vfJLm3Rn4AiSSVKCBSDjC2DmTmXqp4B+G9uq+1zZSvxb8Ga3kT0zjSIGfcy9aMfRvZIbEhg0bWLp0qddlmF5YNm6zfNxm+bhrpLIZqgbx1GT7Ewt3OGHhwoW6efNmr8twRoM28Ez0t3zUn4PIJ4Aa4CXi07ydBaRzsGIXe19cQVbhLGZdLWTkDc17161r4sjezbT9TQ6zZTb+ujDVL2ynO5RH2bL5lC3LGZo3MkPG7sR2l2XjNsvHbZaPu0Yim/6GTAz43VV1b7LH0JV8+tra2rwuwSmlUsoy35WsjjUCPwHKgfcQ049zSOfToedQMu/DLL81xLTLfkXFn7aw+X9jRMLJzxvthqYd0NfvVA1vRji0+xVefHe8t351bDX/N+Fxdi45xJiPxeg49DRVzzQO5aWaIVBRUeF1CaYPlo3bLB+3WT7uGols+ru37JTHELvM5ZvqvPRi9EV26k4CiXG8PnwUSAGH9BB5kscq3yoyJAPYSbDxBbb97mamXpLOhEUnnkcV9r0K+zfC2DlwaCeMPwemXHz8pr39m2HftqfZ+IFXea//H44tVAJwuOMwWzO28qauZ+Vz48nPfCfT3nZ6M2uYoRMKhcjKGpqbO83QsmzcZvm4zfJx10hkM2yzTLhsxowZunv3bq/LOKM0aAOPRh9likzhKt9V+KUd1V+y+7lbObI3mwU3QHo2tOyFHY/BxBVwZPlWXo09zhiZwVnbLiP0SgkFUyG3FLZveY3WGx7iGv93Eo3s46qqqpg5cyYRjfDz6F2c/5cJlIy7jsnn27xwLjiaj3GPZeM2y8dtlo+7RiKbUdkgnj17tlZWVnpdxhmpMlbJ07FnWCKLudC/CPgFoeab2fpQvAc3rwxmvxM2yMME5HEWy9WEaKBCa9kRG0t09zkEKnKYce2DnOv/KdLLPG/Nzc0UFhYC8Zkw7on+gGV/nEzZjPdQtjR9JC/X9KJnPsYtlo3bLB+3WT7uGols+msQp+QcWLFYzOsSzlizfbOZ7ZvN67HX+XHkft7n/xvGF97LuZ/6MJGuYgLpId6MfZU0zWGx7+dAgGxgqbSx1PcqHbMrCM6qZZzvTuKTkbxVR0fHsf/jByTAx/yf5b73/DfR3z2OP/06iuf66Q5BJATdIYh2QcFUCGT0ejozxHrmY9xi2bjN8nGb5eMuF7JJyQaxOX3n+c5jsSzm4ejDZEgJ7/E9gD9tJq9HHyFdbmCx/8qTXpEHXEW2XEV2P4t/nHwnaUAC3Oj/PA984DukP9nJgU3TCWR1kZYVJpAVxReIUvf6ZKJdk0jLKWTCYmHsHBiqG1KjGuVNfZMoURbJoresItiTqtIdgmCD0FYPrXXxhjvQo/3fBbqPcecoE5fPGpoiR5Ddhe0uy8Ztlo/bLB93uZBNSjaIe/szvRm8DMngbwN/y77YPn4S/QN5vMqFvq8w2zf7tM6blpb2lm0BCfCRwJf57TX3oWwmig/FDwhKjOzzd1AgdWQEO9i7aTKhl6eRHz2LOfPmM/m8jLf0Hnd1QMObMcq3N5JRoCy+YgxFhSeOUa6KVbEmtoZwG0x5+WIIBfjfac8RndVEfl4Gy3zLyJZsKtqr2LWxjXBFPsTAlxPFX9YRfyxqJyM7RA4HKZJ9+OkmRjqHYpPY8WweMx+/kHPfeWbNudxbPsYNlo3bLB+3WT7uciGblBxDbPMQDz1VJUSIbDn9G9+qq6uZNm3aaZ2jXRupjD5G3Y69BNdOIjtcyPRpkzh82EdtWyOR7CPkLa5j1lw43LSAHS8U0NkipM1rIeP8g4S7I5S8vJTJtQvIzk9j6iWQnguHq6C5ClrbQlTH9tIdi1CWVcL8xVHGn7ULf6AaOHlITi4wGVgIHL9LNqIRnnrlLoLVs3n/Ddfgd+A34IEYinzM8LBs3Gb5uM3ycddIZTMkC3OcSZYuXaobNmzwugzTh2AwSG5u7pCdT1Wpju1hY80axhcGWDDmLPKlCEgnPtX2y0ATqm/n0M4Z7HsF/OnxaeIKpgJEgB1ALfGFF9s53uhV4mMhJgIzgakM9g8r27f9mhdeyuadH1vB1LTJp3/BQ6RWa6nXeiIaIUqUCBEUZXHHYoryirwuz/RiqD87ZmhZPm6zfNw1UtmMugbxggULdOvWrV6XYfpQUVHBvHnzRvhdu4FngT3ApUAm8CZwBPADc4mvRp4H5DCINWsG5HDNIzz2SD7hj1eTnuFHI4J2+dAuPxoWCAeQcIC0riwyugOkhzoJHW4n1hLDH+nCJxFQQfHTFcgiUipESn34SzvJGANz/LOZJ/PIl/wB1fN89Hnq6lqZVr8QQgHoDKChAF1dUWpmPs+7l199wtzRxg3efHbMQFk+brN83DVS2Yy6BvFZZ52l27dv97oM04dIJEIg4NXw9RjxHuMosBgoGKH3VToO/Z4dj84FScPnj+BP78af0U0gowN/ehhfegRN74KMGGTmU1BUQn5RCf5AEXB0qEonka4agvv301bfRlt9Jh1H/LRomAaFI5pONCBcdu1EFo1761hvVeV/u39D9iNLmesLU3L2HgKZUdKyogSyoqBdrP3VNWz+wJNcOfF85vrmjtC/jxkIbz87pj+Wj9ssH3eNRDbr169n+fLlZ/60ayJyNfAD4t15d6vqt/s6NhQK9bXLOGDbtm0sWrSo/wOHhQ+4xIP3FbLHvp8lN1cnakgj/vFLJz7ueKBzymUSSJ9DwZQ5FEzpub0F2AnsItzexl8eifBcbC9Xv3cq83PjDeMO7eAX+37LrD9cy4Xvfo7C6QuBvznp/FGm/s3XyXr8M7xx8XoazmrgMv9lJxyhqtRqLeNk3FsWXTHDy9vPjumP5eM2y8ddI5FNW1tb0v1nRA+xiPiJ/9f+SuIDPdcCN6hqeW/Hz507V3fs2DGCFRrjEgWeI3jwAH98ZAFN4+s49+pCXn26kYuPXMC51z+IP3ADMK7P16v+iq2/vYrqiU20XVDOFJnCbt1NhAjEhOLqWdRP2MnUnLLEyob+t5ylXut5KvoUkvifEv9Zk5b4X5gwESJIYr46Hz7m++ZztpxNltjyqsYYY4ZOSqxUJyIXAF9T1Xcknn8FQFW/1dvxU6dO1b17945ghWYw1q9fz7Jly7wuYxRoAh6iqeoKnn8ii4vf1k3ZOc8Ct5CsRzqez1LgN+x6+jz2d4whfWaQjK1TiHb4ER8UTIvPyNE6rp5t7/gTSwOLuMB3ASJCRayCv8b+ypgtc5jyysVkZR+fTieQCVmlETLGd5NXmEZuYeDYlHld2kWFVrAltoUQIdJJZ7lvOfN984fx3+jMYp8dt1k+brN83DUS2fTXID4z5oGK3+K/r8fz2sS2Y0TkVhFZJyLrOjo6aGpqoqGhgbq6Opqbm6mqqiIUClFeXk4sFuPoLBTr168HYMOGDcRiMcrLywmFQlRVVdHc3ExdXR0NDQ00NTVRXV1NMBikoqKCSCTCpk2bTjjH0a9btmwhHA5TWVlJa2srNTU1NDY20tjYSE1NDa2trVRWVhIOh9myZUuv59i0aRORSISKigqCwSDV1dUpc005OTkpd01u5pROTc0qAmM3cc2NvyZtwrO0tv4tlZU1Sa8pEAgQiUSpqFjGxEteo7BgE9n1hRSe20DptVuZ8d43Ccx6lPkf3ktORjvn/uLvabg/wF2RH/PgugepfLONsV+9hvNalzNp1U84+8M/Z+I7/4s5H/wJ4y69G3/+YwRr11LxzJts/M0+nvz2btbfE+HJ/9hP9P6F5HzjIpY98HFyv34JFU928+s3f8Pa0Fp27tyZojkN/JqWLVuWcteUSjn1zCdVrimVclqwYEHKXVOq5FRSUjLs19TfyIEzpYf4/cDVqvrxxPOPAuep6j/0drz1ELvNfkv3QoSB3jLw1nyeBuqID98vSDzGANXAQSCPurWXUfv6eCDGhMXbmXLRGkTGAJdzfGhGBOgEOoAg8Rk+WhKPNuI3Oh4VACZwaNcCqp6aQNPZ29h90Yss9y/lPDlv1C6+Y58dt1k+brN83OVCD/GZ0iAe1JAJG0NszEhqBV5BtRZQRM4FFtFjLetT0A3sBzYDNTRsXEHNS4tpv2gH5Qtf4ELfBSz1LT390k+DqhIkSJQofvykkUaAAH78o7bBbowxrkqVBnGA+E11VxDvqloL/K2qbuvt+FmzZumuXbtGsEIzGFu2bOGcc87xugzTB/fyUaAc1ZepWTODA5vPp/mSnVTNf513+K5ipm/mqZ9ZlV8+s5pwMIZkRxKPKL6j3+dE8GVHyExPo9gXo1EjdCUWbVGFvFg+/o5Mutqhq02JtPvo6lD8E9vxT2lHBIqlmCkyhW66qdd6DukhAGLEyCCD9/rfO+AVIN3LxvRk+bjN8nHXSGSzY8cO5s2bd2Y3iAFE5Frg+8T/bvtLVf1mX8cuW7ZMj443Me4Jh8NkZNh0Xa5yO58WVB+n+q/jadx2MfsuXMvhc7bzHv97GCd9zZrRt0eeX0+ZfyMLF2bS1R6guyOX7o6ixNdmutu76e7wE+6EsKaTQRS/AAIiWYi/hLRsIT2nk7ScEOk5HQSyOmmrm8WRmknEohDKbCM8t460aDoZdeNIa8lFfIIqtEWCbH/nH5k1YQJX+K7ot2fZ7WyM5eM2y8ddI5XNqFuY45xzztGjA7KNeyorK5k9+62LRhg3nBn5NKH6R2rWnEXdpuWsW/kH/t/ZNwzqDA31QZ5/6v/48M0ria9SCBACDhMf01wGJFv5rwWoIt6DnUl8PulM4nNLVxD/o5bQHTqbpool+NLSyZ8EmWNiiISAEN2dBex4NMDeYB273v04Vxe+LeliKMOVjarSQQfZZNtwj9NwZnx2Ri/Lx10jlc2oaxAvWrRIj97laNzT2tpKfv7Alhg2I+/Mymc3qk/y0I/mc+GtM5maPnlAr4pG4Bd3PsVHbmsmN+36YawvBpQDGzh+06CfeOM5i3jju5vO1lwqHjmP8rRDtE1tIHogHV+z4NduAoTpjqbTPTadorPDnDN3GrNl9qAXRVFV2mmnjTaaYoeobK+m6XAQPZxJrCWd9PZcwhkdSEYUyYjiz4wyIT+T8VOKKfaNpViKySOv1wazqtJKKy3awmEO06zNhAhxie+SAS8nngrOrM/O6GP5uGsksmlrayM/P//MX6luMILBoNclmCRaWlrsh5LDzqx8ZiDyad7+rl/x+79E+dR7BtYgfuKBPZz3wRfJTfvGMNfnAxYkHn3LzO9k8Y3bmdW4h46DAXLnhcgqykrM1DEJaKW9qZatT/uofWk3G/y7CE3yEcvwI1lRJDNKfnYWGekBjtBCxBdBfAoCGvIT2ZtLbF8eaZFMssgkh1zK8t7GwoJssooiZE3rIj2njWj3HiKh/UTCQnfIz+HdMQ7/tY7yaD2HpICOaSH8U9qgLZ3ooQzSDkXIaO8ggyA+ySFAEWkUk8lZZEYz+d+MzaRfuY9VE644pSEtZ5oz67Mz+lg+7hqJbPobSpuSDWL7k5/bMjMzvS7BJHEm5lMybS6BJw7R2RkhMzP5j7U9r0TonPwbFk/8HKc3E8ZQygSWkDtuCbl9tBtzimH62xs5b1wh0e5Kggd2EQm1E+nMpLvjLI60TCDcBTmxPPz40Vj8xr9ABhRMayf/4q0E0suJT393dAp6P/EhHulALjAHuODY/knH3r2FWHQNLXuDHKnJIyM/QvacI2QXF5GWfQ4iU4hPpdcA1AM7gCAXteZS/vQV/PHgPrqXvMy15y5hmm9qyv6MPhM/O6OJ5eMuF7JJyQaxMWa0OY+Vq37AY3+ezgfe33dvbPtBWLPlSa65dR4iJSNY31BKw582nzGTjq7gFwa2MYlHiQ/LOLmxGQNyiPdSf5hT+7FfgM//LopmQNEM7eU9APISjznHtmTmt7H0/U+yRA9Ts24lL97dzp99LyNjukib2crY2cLM3CnMklnkSd4p1HUiVaVGazjIQbLIIptsMqPZpIWyKMzOwx9IzYa4Meb0pWSDOBXHRaeSzs5Or0swSZyZ+fiZOXEcfzm8n66OBaT3MotZJAwvPlDP3E8+QbH8aORLHAK9Z5MBLE08RsJgGpV5wAcQiTL13DV89NwKoIBQywqaq6ZR+0QHe9uaWBvbQXfZYdIXHyJngjLTNxM/flq1lVZt48ihMJ216eQUBJg7dQKz/DMplEIg/vN+l1ay4eALdL+axpjGPHIC6RxGCROgy+8jnKnsP5jFjJklrLpywbA1jM/Mz87oYfm4y4VsUrJB7POdKStSj04FBQVel2CSOFPzEbmKJat+xuuPLuOS6wtP2BcJwxs/ieL/yL+yNPP73hQ4BM7UbOJDM96WeLSRVbCOrGXPUbYMYCaqBbQ1TGL/m3NpfhKaaUFFyGASJZLJ7OJ0Cif6adlzhH2rG3lWXyDmayFz0iHCh3LIaRvHWcVLmXHxOHLHTUq8XwRoJj4byGFgJ1srtvKzn+xh8qyZXHvl/H4bxrEobHu9hfraNs6/tIwxE/xJjz9z8xkdLB93uZBNSjaIo9Fo/wcZzxw4cMBubHDYmZvPOM6dMIZfBrezou1CMhJ/gY+EYd1P4dDffpMVxR8kIGO8LfM0nLnZ9JQHrEw8FNiDSCv5ZZ3kl3UQH/Zx9NGc+BoDopSRy3zGArOJxQo5UhMgqxAye400AJQkHgDnsWBehLPnvsrmiqf49U/fxF8wjbKZJcyeOZZJxWPx+QRVpby6jjWra+nsCjNrxS6mXH6QP79YxpED4yksKeGyy2ZSWjyGjo5udu1uYu/uVg40tqP7lUXLp3HO+WPJKhjOf0NzKlLj85OaXMgmJaddmzNnju7cudPrMkwfbHJ0t53Z+bzMbxvXMPfpL7H4I754Y/hn0HL948wtfoXpvj7X8zkjnNnZuEX1IIc7nqR2dwv1u7OpPziODsYQ7RZmTA5y2eU15GenAecCs4F9qO6h9uAu3nwxQKg5jUBOhJJpXYyfkUnpuFIi3QW80NBC7Wtp5LeOY0HeHGZflEXBFI8v1gD2+XHZSGSTEks3D9ZZZ52l27dv97oM0wdbPtNtZ3Y+UWpjX+e1X3+Eq6+ZzdaHIOuGnUTH/jdLfd8l3jt55jqzs3FZJ/G5orcT740+G1jBwP6IGgGOAC1UVb3CzJkCdNGsLWxoPcSu1Usorb+Qt72rjDEDmxXQDBP7/LhrJLLpbx7ilGwQL1++XNetW+d1GcYYD6g+wD3NbUz9r09x1hcO81rhd3if/52IXOp1aWZUUeAgMX2Ziq61PPP4BUxomsc1q2aSX9r3fS6RMDTWhthSW83u2iYinZI4F4Acu2m8LH08i2ZOoXR2JjmJUSHdIQgdhkOH2qlpbqS0ZAwzZxURGGDHmyq01kLDBkjPhckXQZr3s2EZM2RG3Up18+fP1/Lycq/LMH1Yv349y5Yt87oM04czP5/9vBr9H6b5Psfvow/wSX8GafL/vC5qSJz52aS2vvM5jOrv2BnK4YlHx5HZUkRWejo+fPjFhw8f0ViMw9qEPyNEflk7Z03O5qyJOWTnBIjPC+1PPHzEVKjs3Mfm3ftp3ZUHBwuISQRfdheBog5yi2KUFeZRcyCTA1WZaNhHgRQyI7+MsqIiAplCIBP86fF5qo/sg5Y9SqM2Uje5nOiSWrQlg+irZYzvmsjiRaXMODcTXx/3FKrGG+LB/fFH5xHw+UH8x7/mlMD4ReDlPe/2+XHXSGUz6hrECxYs0K1bt3pdhjHGIyH9Pl/o3sm30uYyRj4CjPW6JGOAdcDrtOnFdHMIqAYOE1PFh48CGYtPCoAxQD7xIT7CiTcaHr/J8Oj3XRoijQxEAhxvOEvi/M2owmEKWHvER21LF9opEI4/Ap3ppE2sJ39KDTN9mcz0lZAp8W7hiMLeKLyxMYvGDWX4fAKJRVVE4m0HjQEi+AvDBCZ0kl0axZcfoSMWQqLgiylElY66LIo3L2JW+gymXeynaOaxUxkzInbs2MG8efNGV4N46tSpunfvXq/LMH2w39Ldlhr5vERYa8iQscDVXhczZFIjm9Q1sHzCwF+Jz34xHSgY7rI4OpsHbCS+omC8pxn8dGuMAMWITALKON4IB+gGaoDdQB3Hh27kJGqfTnyc9S5gF6rtdBNBUdLJQMSf2J+GapB6reG1kHL4lYuYsftcitILQCCqEdppJ6hBohqlNGM8JZMzyJ8M+RMZ8JCP/tjnx10jkc2ovKlu7ty5umPHDq/LMMZ4JgL8CPh/uLM8szGpIki893k38c/arMSjlxVxThADNtCpr1Guu6iJjaGbbCCPPJlIoYzHT4Cdod0E64Tuumxy60opCZeS78sjm2xE4mMuomHInwyz3kGfQzmM6WlUNoinTZum1dXVXpdh+rBp0yYWLVrkdRmmD5aPuywbt1k+gxEGyoFWoI14I7uT473QoBqghUxqYkGC7CfMQfwE8RElQoziqi8RenY+ExbB5Av7H4Jh+bhrJLIZlQ1i6yF2WyQSIRBIyTVhUoLl4y7Lxm2Wz1DrBg4Ah4Bc4sNL8oE0otrKZv0qTepnxsZv0PRqDtMvh3Hz+z7bYPJproZdT4AvDXInQNkyyCs9xasIQVMFNO2IN9onLIai2d7eYOiakfjs9NcgTslPbldXl9clmCR27drFvHnzvC7D9MHycZdl4zbLZ6ilAZMSjxP5JZ8lciet+gZbF3+crkXvYO/dN1IwTUjvY+TGQPIJt0H57yFWGGTnzQ/jC6Qx+8AyatfPInQgPjYjLSc+Ld3RR0Ze/ObCrnbobj/+NdyWuIosiMxpoPLaZ4hEoXXzFWS+XIbGhOyxMHYOZJdAVhH4U7JV1r/esjnaXztSN1+mZA/x7NmztbKy0usyTB+CwSC5ublel2H6YPm4y7Jxm+XjlSj7Yj9mQ0MNs1//LvPf2/tRyfKJxaDyL9C0v4PK9/yJKYVvcqGvDCGdWm2gPBbgIKVkah7TIjMYH5xCQXAckaCfcFt8HHNaDqTnxL+mZSu1WZVU6B/IlkpKJJ+ZshghRrVupUp9NOlszmq+gPG7zyJ8KEDoEMQiJxXWozEYyIDc0nhPdV5ZvDEuAtEIdByEtgYINkB3BxTPg7FzIZB+ev+yoWZo3ApZY6FgGn3+snG6emZzeDfUrImPE9dYvId+xpWn/96jsofYuK2pqcn+o+Ewy8ddlo3bLB+v+Jnsu42DpV9i/8EDzI2M77Wnta98VOGluzrZfdVTjLl6Jx/0x8iWm4D4+IuZEmGmbzOwibA2c8j/PAcz2thb1EE3AaKahl+6ySBIgO74OVGKpIgrfNeRKf8IHJ8qY54o89hFVNewb+xD7CzqpF1z6WQMxXIWc2UBk2QSclLXaCQcn+e5tS7eSA23xreLH3LGxRvKZedCIBOatsO230K0G8QXb8zmJHqhs4r6nrkj2h0/94EtEO2CrEIYtyDeMN7xKERCx4/1BeLvdfSRlgPjz4n3mA+Earzx3hWE3dtb8DflEjoMhdPh7A9AZ2ZbfI7uhpx4r30Epq+M7+9Pdyh+3T2vMy8veWEp2UO8ePFi3bhxo9dlmD40NTVRXFzsdRmmD5aPuywbt1k+3gpqJWt2/IHZ+77MzCvfur+vfLb+Vnl+8T3cMidMrkwG3kV8Wrq+KPEbAEOJRweQRXy+86xBVq3AfqAR1f0c0u3sZx9H9AhKz/aZECENIZMCxlEk4xkrZWSRRxttNGtL/EELQYUMWUSpTKJMyiiIFHFknxA6FF9AJXQ43rhGgcTU0kebgj4/lMyHcefAkbQm9saeIMRLZDKWMbKEcXI++TIFiPdMR8MQ6Yw3QLuCsH9j/GteGUy5GDLz4+cNt8H+TXC4Mt7rK774e6cnhp+EIi3MurCAzKIutukfOKDPkocPiFKnJRTJVZzbfRENf82grT7xL+KHMVOON5APlnNsXyArPmxl2SdO/NdOtjBHSvYQp2IjP5V0d3d7XYJJwvJxl2XjNsvHW7kym+zZWzj0vDLj7fKWsae95dOwEapyynnP7B3kyleAogG8kxBv+A628dvXuUqBUkQWUSzvoPdfqRQI0aVBDug+Dug+tuguOjXIGMmniEKKZDozpZAswrSxliP6MjtimeyWYrqnZEC8HUuu5FJKKcVSTBddtGs7HXTQTjtt2oaPA5RKBQUaoFguZoJ8nxbqadTX2Ra7iwhN8cpFCGWOIZhRQtuYYqIEyJqVxVLfUsbXzWbXE0K4Ld7gzsiHcecosfNqqPCV00EHOShjaCOfNjJaG9mcf4DOWIQJchlX+H6ASA6gqL7JAX2cDWkPse+KmRTLEqbLdCZFp9BZm8Hhqngje/wimPkOpUkOUqM1dP9pJkdqCxnz1iHovXKiQSwiHwC+BpwFrFDVdT32fQX4GPElef6fqj7lSZFmyMRiMa9LMElYPu6ybNxm+XiviIW0L99A3dplTFpx4r6T8wk1w77XYnR/7FdM8V3PwBrDXhEgm3TJZrKMYzLJF7Eo5BIKRZnGbi7kDeI92vHfEDr1EC26myBtZJFGiaSTQTrpkk4GAdJkFvBx4jN6xI1jFuNkFvDhHu+ixGcB2Q3sBTrp1Gb26r1sLDtIx/tzyGMGnXoYkYMcJkSRFHGBTCSDTLpIo1PH0sEM9geFBQVLyJOThzUIIkuZIEuZQAeqq2lmM036Ai9JGgcmTyAy+egvJjFyY01MlXZKxc8bV7eSdv/3WP73A7srz4kGMbAVeC/ws54bRWQ+cD1wNvEldJ4VkTmqGk12so6OjuGq0wyB7OxhGpVvhoTl4y7Lxm2Wj/fm+D7OK0v/mYZf/PwtDeKe+cRisPkBOHjzX3ibPxfo9a/oZzgBZiYex2UKTBiSmRsEmJB4XHjs3HMF5hJBtZGDlJNLCdkynRNXQYz3r49JPC3Ibe6lMXyybESupYhrKZIoc3yVwGbic1nHV16E84iP/S6hK+Nf2TF+Oy1751MwNX5TXTJOzIKnqttVtbeJg98NPKSqYVXdQ3x9yBW9HHeCaDRpe9l47PDhw16XYJKwfNxl2bjN8vFeuhQRkDAFsyM07TxxX898yv8Ppr4zTHrWE5T4bhrZIkeFACJljJO3ky2LiPc2990KH/xnxw/MAz5IvDf7FuAm4DJgHCDMkH/Ed/X/R+UTAxtG60SDOImJwL4ez2sT25KyidHdVlZW5nUJJgnLx12WjdssHzdMkLfTfunvqH7hxO1H86lbB5mFsGbKg5zvmwVMG/EazYmG47MjUsLZ6bOpLX2d5ur+jx+xBrGIPCsiW3t5vHuIzn+riKwTkXVNTU00NTXR0NBAXV0dzc3NVFVVEQqFKC8vJxaLsWHDBgDWr18PwIYNG4jFYpSXlxMKhaiqqqK5uZm6ujoaGhpoamqiurqaYDBIRUUFkUiETZs2nXCOo1+3bNlCOBymsrKS1tZWampqaGxspLGxkZqaGlpbW6msrCQcDrNly5Zez7Fp0yYikQgVFRUEg0Gqq6tT5prWrVuXcteUSjm9/vrrKXdNqZLTnj17Uu6aUimnnvmkyjWdiTml7buQ9o5niWS0ULG2ltbWVnaUV/LyA/X86fY6QofgYNGrTJBX2L1p8RlxTamYU89r2rRp07Bc00T+gQln38vOJ6L0t4KxU9Ouichq4ItHb6pL3FCHqn4r8fwp4Guq+mqy89jSzW6LxWL4bM1KZ1k+7rJs3Gb5uOOV6Cc4O/xdyn9eQFZRfA7byRfFKJkbz+ehyP/wbv9YsuTvPK7UwPB+dvbHfsaax2eQ05bHtR++oM9p11z/5D4KXC8iGSIyHZgNvNHfizo7O4e9MHPqbI5ot1k+7rJs3Gb5uGOq/C07Mn7O3OviizwsvQX2tW8EoFZrmevbQZZ8yNsizTHD+dmZ4PsY4666j4Nv5Cc9zokGsYj8jYjUAhcAjyd6glHVbcDvgHLgSeAz/c0wAZCZmTmc5ZrTtHTpUq9LMElYPu6ybNxm+bhjou8yOtlM0QxIS8zKtXTpUmpiNTwX+SHnyN/Qc/U4463h/ewEODv9vTRPey7pUU40iFX1EVWdpKoZqjpeVd/RY983VXWmqs5V1ScGeL7hK9actqNjgYybLB93WTZus3xcIqTLeA7rbgDCGuaPG35NA9/nI4E5BOQqj+szPQ33Z2es/A0LPrw66TFOjSEeKsuXL9d169b1f6AxxhhjUtJhraAidg8Zcjkt+hDLfecyRm4gvsSyGW2O6BoKfJecsWOIT4ktzOG2o3exGjdZPu6ybNxm+bilSOYRYT+5up4r/D+m6s0Lscawm0biszNGLk66PyV7iBcuXKibN2/2ugzTB7sT222Wj7ssG7dZPm6zfNw1EtnU19czceLE0dVD3NbW5nUJJomKigqvSzBJWD7usmzcZvm4zfJx10hks3PnzqT7U7JBnJaW5nUJJonp06d7XYJJwvJxl2XjNsvHbZaPu1zIJiUbxJFIxOsSTBL19fVel2CSsHzcZdm4zfJxm+XjLheySckGsd/v97oEk0RRUZHXJZgkLB93WTZus3zcZvm4y4VsUrJBHIvFvC7BJGGzgLjN8nGXZeM2y8dtlo+7XMgmJRvExm12l6/bLB93WTZus3zcZvm4y4VsvK9gGIiI1yWYJOymR7dZPu6ybNxm+bjN8nGXC9mk5DzEItIG7PC6DtOnYqDJ6yJMnywfd1k2brN83Gb5uGukspmqqiW97QiMwJt7YUdfEy8b74nIOsvHXZaPuywbt1k+brN83OVCNik5ZMIYY4wxxpiBsgaxMcYYY4wZ1VK1QfxzrwswSVk+brN83GXZuM3ycZvl4y7Ps0nJm+qMMcYYY4wZqFTtITbGGGOMMWZAUq5BLCJXi8gOEdklIv/sdT2jnYj8UkQaRWRrj21FIvKMiFQmvhZ6WeNoJSKTReQFESkXkW0i8tnEdsvHASKSKSJviMimRD53JLZPF5HXEz/jfisi6V7XOlqJiF9E3hSRPyeeWzaOEJFqEdkiIhtFZF1im/1sc4SIFIjI70WkQkS2i8gFXueTUg1iEfEDPwKuAeYDN4jIfG+rGvXuBa4+ads/A8+p6mzgucRzM/IiwBdUdT5wPvCZxOfF8nFDGLhcVRcBi4GrReR84DvA/6jqLKAZ+Jh3JY56nwW293hu2bhlpaou7jGdl/1sc8cPgCdVdR6wiPjnyNN8UqpBDKwAdqnqblXtAh4C3u1xTaOaqr4IHD5p87uB+xLf3we8ZyRrMnGq2qCqGxLftxH/gTQRy8cJGhdMPE1LPBS4HPh9Yrvl4xERmQS8E7g78VywbFxnP9scICJjgEuBewBUtUtVW/A4n1RrEE8E9vV4XpvYZtwyXlUbEt/vB8Z7WYwBEZkGLAFex/JxRuJP8huBRuAZoApoUdVI4hD7Geed7wP/BMQSz8di2bhEgadFZL2I3JrYZj/b3DAdOAj8KjHk6G4RycHjfFKtQWzOMBqf5sSmOvGQiOQCDwOfU9XWnvssH2+palRVFwOTiP8FbJ63FRkAEXkX0Kiq672uxfTpYlVdSnwI5WdE5NKeO+1nm6cCwFLgJ6q6BGjnpOERXuSTag3iOmByj+eTEtuMWw6ISClA4mujx/WMWiKSRrwx/L+q+ofEZsvHMYk/J74AXAAUiEggsct+xnnjIuA6EakmPjTvcuJjIi0bR6hqXeJrI/AI8V8o7WebG2qBWlV9PfH898QbyJ7mk2oN4rXA7MSdvunA9cCjHtdk3upR4MbE9zcCf/KwllErMebxHmC7qv53j12WjwNEpEREChLfZwFXEh/n/QLw/sRhlo8HVPUrqjpJVacR/+/M86r6YSwbJ4hIjojkHf0euArYiv1sc4Kq7gf2icjcxKYrgHI8ziflFuYQkWuJj+3yA79U1W96W9HoJiIPApcBxcAB4N+BPwK/A6YAe4EPqurJN96ZYSYiFwMvAVs4Pg7yX4iPI7Z8PCYiC4nfWOIn3nnxO1X9uojMIN4rWQS8CXxEVcPeVTq6ichlwBdV9V2WjRsSOTySeBoAfqOq3xSRsdjPNieIyGLiN6SmA7uBm0n8nMOjfFKuQWyMMcYYY8xgpNqQCWOMMcYYYwbFGsTGGGOMMWZUswaxMcYYY4wZ1axBbIwxxhhjRjVrEBtjjDHGmFHNGsTGGDOERCQqIht7PKZ5XdNQEZElInJP4vubROSuk/avFpHlSV7/kIjMHu46jTFmsAL9H2KMMWYQQonllt8isRiKqGqst/1ngH8BvnEar/8J8E/AJ4amHGOMGRrWQ2yMMcNIRKaJyA4RuZ/4almTReRLIrJWRDaLyB09jv1XEdkpImtE5EER+WJi+7GeVxEpTiwZjIj4ReS7Pc7194ntlyVe83sRqRCR/000xhGRc0XkFRHZJCJviEieiLyYmCj/aB1rRGTRSdeRByxU1U0DuObrevSQ7xCRPYldLwFv77G8sTHGOMF+KBljzNDKEpGNie/3AP8IzAZuVNXXROSqxPMVgACPisilQDvxZYAXE//ZvAFY3897fQw4oqrnikgG8LKIPJ3YtwQ4G6gHXgYuEpE3gN8CH1LVtSKSD4SIL+F9E/A5EZkDZPbS8F1OvEHf04cSKx4eNQtAVR8lvgwrIvI74K+J7TER2QUsGsC1GWPMiLEGsTHGDK0ThkwkxhDvVdXXEpuuSjzeTDzPJd5AzgMeUdWOxOseHcB7XQUsFJH3J56PSZyrC3hDVWsT59oITAOOAA2quhZAVVsT+/8P+DcR+RJwC3BvL+9VChw8adtvVfUfelzr6p47ReSfiP97/KjH5kagDGsQG2McYg1iY4wZfu09vhfgW6r6s54HiMjnkrw+wvEhbpknnes2VX3qpHNdBoR7bIqS5Oe9qnaIyDPAu4EPAst6OSx00nsnJSJvBz4AXHrSrszEuYwxxhk2htgYY0bWU8AtIpILICITRWQc8CLwHhHJSozXXdXjNdUcb6S+/6RzfUpE0hLnmiMiOUneewdQKiLnJo7P6zGe927gTmCtqjb38trtJIZE9EdEpgI/Aj6gqic3fufw1qEXxhjjKeshNsaYEaSqT4vIWcCrifvcgsBHVHWDiPwW2ER8WMHaHi/7HvA7EbkVeLzH9ruJD4XYkLhp7iDwniTv3SUiHwJ+KCJZxHtq3w4EVXW9iLQCv+rjtRUiMkZE8lS1rZ/LvAkYC/wxcY31qnqtiIwnPoRifz+vN8aYESWq6nUNxhhjTiIiXyPeUP3eCL1fGbAamNfXtHAi8o9Am6refYrv8Y9Aq6rec8qFGmPMMLAhE8YYM8qJyN8BrwP/2s8cyT/hxLHJg9UC3HcarzfGmGFhPcTGGGOMMWZUsx5iY4wxxhgzqlmD2BhjjDHGjGrWIDbGGGOMMaOaNYiNMcYYY8yoZg1iY4wxxhgzqlmD2BhjjDHGjGr/P5ovnbbCiudxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<MNELineFigure size 720x252 with 2 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.notch_filter([25, 50], filter_length='auto', phase='zero') #250/2 based on Nyquist Theorem\n",
    "#observe that the 50Hz noise is now gone, yay!\n",
    "raw.compute_psd().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Band pass filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 4 - 62 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 4.00, 62.00 Hz: -6.02, -6.02 dB\n",
      "\n",
      "Effective window size : 2.048 (s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chaklam/DSAI/Environments/teaching_env/lib/python3.8/site-packages/mne/viz/utils.py:137: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  (fig or plt).show(**kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<MNELineFigure size 720x252 with 2 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#since we use 6hz, 10hz, and 15hz, \n",
    "#we will take five harmonics, which maximum freq is 15 x 4 = 60\n",
    "#we will take +- 2 at each end, thus 6-2 = 4, and 60+2 = 62\n",
    "#these numbers are made up by me; you can try different numberes\n",
    "raw.filter(4, 62, method='iir')\n",
    "\n",
    "#this should also automatically remove any slow drift under 1Hz\n",
    "raw.compute_psd().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Epoching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mne import Epochs, find_events\n",
    "\n",
    "def getEpochs(raw, event_id, tmin, tmax, picks):\n",
    "\n",
    "    #epoching\n",
    "    events = find_events(raw)\n",
    "    epochs = Epochs(raw, events=events, event_id=event_id, \n",
    "                    tmin=tmin, tmax=tmax, baseline=None, preload=True,verbose=False, picks=picks)  #8 channels\n",
    "    print('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100)\n",
    "\n",
    "    return epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60 events found\n",
      "Event IDs: [1 2 3]\n",
      "sample drop %:  0.0\n"
     ]
    }
   ],
   "source": [
    "#this one requires expertise to specify the right tmin, tmax\n",
    "event_id = {'6Hz': 1, '10Hz' : 2, '15Hz' : 3}\n",
    "tmin=-0.5\n",
    "tmax=3\n",
    "eeg_channels = mne.pick_types(raw.info, eeg=True)\n",
    "picks= eeg_channels\n",
    "epochs = getEpochs(raw, event_id, tmin, tmax, picks=picks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. EDA: Checking stimuli-specific PSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective window size : 8.224 (s)\n",
      "Effective window size : 8.224 (s)\n",
      "Effective window size : 8.224 (s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<decorator-gen-321>:4: FutureWarning: NOTE: psd_welch() is a deprecated function. Function ``psd_welch()`` is deprecated; for Raw/Epochs/Evoked instances use ``spectrum = instance.compute_psd(method=\"welch\")`` instead, followed by ``spectrum.get_data(return_freqs=True)``.\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<MNELineFigure size 720x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<MNELineFigure size 720x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<MNELineFigure size 720x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 4))\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
    "\n",
    "psd1, freq1 = psd_welch(epochs['6Hz'], n_fft=1028, n_per_seg=256 * 3)\n",
    "psd2, freq2 = psd_welch(epochs['10Hz'], n_fft=1028, n_per_seg=256 * 3)\n",
    "psd3, freq3 = psd_welch(epochs['15Hz'], n_fft=1028, n_per_seg=256 * 3)\n",
    "\n",
    "logpsd1 = 10 * np.log10(psd1)\n",
    "logpsd2 = 10 * np.log10(psd2)\n",
    "logpsd3 = 10 * np.log10(psd3)\n",
    "\n",
    "log_psd1_mean = logpsd1.mean(0)\n",
    "log_psd1_std = logpsd1.mean(0)\n",
    "\n",
    "log_psd2_mean = logpsd2.mean(0)\n",
    "log_psd2_std = logpsd2.mean(0)\n",
    "\n",
    "log_psd3_mean = logpsd3.mean(0)\n",
    "log_psd3_std = logpsd3.mean(0)\n",
    "\n",
    "axes.plot(freq1, log_psd1_mean[[0,2], :].mean(0), color='b', label='6Hz')\n",
    "axes.plot(freq2, log_psd2_mean[[0,2], :].mean(0), color='r', label='10Hz')\n",
    "axes.plot(freq2, log_psd3_mean[[0,2], :].mean(0), color='g', label='15Hz')\n",
    "\n",
    "axes.set_title('')\n",
    "axes.set_ylabel('Power Spectral Density (dB)')\n",
    "axes.set_xlim(4, 30)\n",
    "axes.set_ylim(-170, -100)\n",
    "axes.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we can see the stimuli specific PSDs oscillating at its frequencies and harmonics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Canonical Correlation Analysis\n",
    "\n",
    "Canonical correlation analysis is likely the most standard process to analyze which stimuli belongs to which frequency.  Even it is a very old technique, but it works great here.\n",
    "\n",
    "Note: The code below may look hard, but once you look closely, it's not really hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from scipy.stats import pearsonr, mode\n",
    "from sklearn.cross_decomposition import CCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we shall create a function that would help us filter each sub-band.\n",
    "\n",
    "For example, we can try the band of 4Hz to 8Hz, and then do a correlation.\n",
    "\n",
    "This filterbank is much more accurate than non-filterbank version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filterbank(eeg, fs, idx_fb):    \n",
    "    if idx_fb == None:\n",
    "        warnings.warn('stats:filterbank:MissingInput '\\\n",
    "                          +'Missing filter index. Default value (idx_fb = 0) will be used.')\n",
    "        idx_fb = 0\n",
    "    elif (idx_fb < 0 or 9 < idx_fb):\n",
    "        raise ValueError('stats:filterbank:InvalidInput '\\\n",
    "                          +'The number of sub-bands must be 0 <= idx_fb <= 9.')\n",
    "            \n",
    "    if (len(eeg.shape)==2):\n",
    "        num_chans = eeg.shape[0]\n",
    "        num_trials = 1\n",
    "    else:\n",
    "        _, num_chans, num_trials = eeg.shape\n",
    "    \n",
    "    # Nyquist Frequency = Fs/2N\n",
    "    Nq = fs/2\n",
    "    \n",
    "    passband = [6, 14, 22, 30, 38, 46, 54, 62, 70, 78]\n",
    "    stopband = [4, 10, 16, 24, 32, 40, 48, 56, 64, 72]\n",
    "    Wp = [passband[idx_fb]/Nq, 90/Nq]\n",
    "    \n",
    "    #print(\"Wp: \", Wp)\n",
    "    Ws = [stopband[idx_fb]/Nq, 100/Nq]\n",
    "    \n",
    "    #print(\"Ws: \", Ws)\n",
    "    [N, Wn] = scipy.signal.cheb1ord(Wp, Ws, 3, 40) # band pass filter StopBand=[Ws(1)~Ws(2)] PassBand=[Wp(1)~Wp(2)]\n",
    "    [B, A] = scipy.signal.cheby1(N, 0.5, Wn, 'bandpass') # Wn passband edge frequency\n",
    "    \n",
    "    y = np.zeros(eeg.shape)\n",
    "    \n",
    "    if (num_trials == 1):\n",
    "        for ch_i in range(num_chans):\n",
    "            #apply filter, zero phass filtering by applying a linear filter twice, once forward and once backwards.\n",
    "            # to match matlab result we need to change padding length\n",
    "            y[ch_i, :] = scipy.signal.filtfilt(B, A, eeg[ch_i, :])\n",
    "        \n",
    "    else:\n",
    "        for trial_i in range(num_trials):\n",
    "            for ch_i in range(num_chans):\n",
    "                y[:, ch_i, trial_i] = scipy.signal.filtfilt(B, A, eeg[:, ch_i, trial_i])           \n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we shall create a function that would simply create a repeating sin and cos wave representing our target frequencies.  We need to do this because we need to do a correlation between our given signal, and this reference signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cca_reference(list_freqs, fs, num_smpls, num_harms=3):\n",
    "    num_freqs = len(list_freqs)\n",
    "    tidx = np.arange(1, num_smpls + 1) / fs  # time index\n",
    "\n",
    "    y_ref = np.zeros((num_freqs, 2 * num_harms, num_smpls))\n",
    "    for freq_i in range(num_freqs):\n",
    "        tmp = []\n",
    "        for harm_i in range(1, num_harms + 1):\n",
    "            stim_freq = list_freqs[freq_i]  # in HZ\n",
    "            # Sin and Cos\n",
    "            tmp.extend([np.sin(2 * np.pi * tidx * harm_i * stim_freq),\n",
    "                        np.cos(2 * np.pi * tidx * harm_i * stim_freq)])\n",
    "        y_ref[freq_i] = tmp  # 2*num_harms because include both sin and cos\n",
    "\n",
    "    return y_ref"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we shall find the correlation between the given signal and the reference frequency signal, by looping across events, and filterbanks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fbcca(eeg, list_freqs, fs, num_harms=3, num_fbs=5):\n",
    "    fb_coefs = np.power(np.arange(1, num_fbs + 1), (-1.25)) + 0.25\n",
    "\n",
    "    num_targs = len(list_freqs)\n",
    "    events, _, num_smpls = eeg.shape  # 40 taget (means 40 fre-phase combination that we want to predict)\n",
    "    y_ref = cca_reference(list_freqs, fs, num_smpls, num_harms)\n",
    "    cca = CCA(n_components=1)  # initilize CCA\n",
    "\n",
    "    # result matrix\n",
    "    r = np.zeros((num_fbs, num_targs))\n",
    "    results = np.zeros(num_targs)\n",
    "    r_mode = []\n",
    "    r_corr_avg = []\n",
    "\n",
    "    for event in range(eeg.shape[0]):\n",
    "        test_tmp = np.squeeze(eeg[event, :, :])  # deal with one event a time\n",
    "        for fb_i in range(num_fbs):  # filter bank number, deal with different filter bank\n",
    "            for class_i in range(num_targs):\n",
    "                testdata = filterbank(test_tmp, fs, fb_i)  # data after filtering\n",
    "                refdata = np.squeeze(y_ref[class_i, :, :])  # pick corresponding freq target reference signal\n",
    "                test_C, ref_C = cca.fit_transform(testdata.T, refdata.T)\n",
    "                # len(row) = len(observation), len(column) = variables of each observation\n",
    "                # number of rows should be the same, so need transpose here\n",
    "                # output is the highest correlation linear combination of two sets\n",
    "                r_tmp, _ = pearsonr(np.squeeze(test_C),\n",
    "                                    np.squeeze(ref_C))  # return r and p_value, use np.squeeze to adapt the API\n",
    "                if r_tmp == np.nan:\n",
    "                    r_tmp = 0\n",
    "                r[fb_i, class_i] = r_tmp\n",
    "        rho = np.dot(fb_coefs, r)  # weighted sum of r from all different filter banks' result\n",
    "        print(\"rho: \", rho)\n",
    "        result = np.argmax(rho)\n",
    "        print(\"result: \", result)\n",
    "        r_mode.append(result)\n",
    "        print(\"correlation: \", abs(rho[result]))\n",
    "        r_corr_avg.append(abs(rho[result]))\n",
    "    print(\"====Most recurrent class: ====\", mode(r_mode)[0][0])\n",
    "    print(\"====Average correlation: =====\", np.mean(r_corr_avg))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our code, let's find the correlation between our signal and the three given frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6hz: =============================================\n",
      "rho:  [0.25755617 0.33524926 0.65949178]\n",
      "result:  2\n",
      "correlation:  0.6594917796491001\n",
      "rho:  [0.33537148 0.67365617 0.51072422]\n",
      "result:  1\n",
      "correlation:  0.6736561691979026\n",
      "rho:  [0.30174884 0.27414146 0.69992852]\n",
      "result:  2\n",
      "correlation:  0.6999285192845894\n",
      "rho:  [0.48317676 0.39900272 0.43324982]\n",
      "result:  0\n",
      "correlation:  0.4831767572718295\n",
      "rho:  [0.43249231 0.37407133 0.41424319]\n",
      "result:  0\n",
      "correlation:  0.432492312109832\n",
      "rho:  [0.42429458 0.40517765 0.60926208]\n",
      "result:  2\n",
      "correlation:  0.609262081702428\n",
      "rho:  [0.59704393 0.42768495 0.46383847]\n",
      "result:  0\n",
      "correlation:  0.5970439317369973\n",
      "rho:  [0.49651624 0.35864786 0.41599858]\n",
      "result:  0\n",
      "correlation:  0.496516237226185\n",
      "rho:  [0.38550271 0.38116319 0.84524035]\n",
      "result:  2\n",
      "correlation:  0.845240348884251\n",
      "rho:  [0.58889701 0.33992156 0.62875843]\n",
      "result:  2\n",
      "correlation:  0.6287584342271257\n",
      "rho:  [0.5747352  0.50057292 0.48143046]\n",
      "result:  0\n",
      "correlation:  0.574735201636837\n",
      "rho:  [0.57439678 0.51244482 0.38882057]\n",
      "result:  0\n",
      "correlation:  0.5743967752951001\n",
      "rho:  [0.4260211  0.48411959 0.35354072]\n",
      "result:  1\n",
      "correlation:  0.4841195941028981\n",
      "rho:  [0.59613644 0.33859397 0.53001501]\n",
      "result:  0\n",
      "correlation:  0.5961364350776049\n",
      "rho:  [0.60765034 0.50667906 0.42863908]\n",
      "result:  0\n",
      "correlation:  0.6076503415898952\n",
      "rho:  [0.50786287 0.51995506 0.71915393]\n",
      "result:  2\n",
      "correlation:  0.7191539291655478\n",
      "rho:  [0.54166052 0.56910599 0.58607149]\n",
      "result:  2\n",
      "correlation:  0.5860714911204388\n",
      "rho:  [0.69432026 0.50084182 0.52038668]\n",
      "result:  0\n",
      "correlation:  0.6943202640663554\n",
      "rho:  [0.50604214 0.30222192 0.49311289]\n",
      "result:  0\n",
      "correlation:  0.5060421374065825\n",
      "rho:  [0.54183778 0.27544399 0.37274939]\n",
      "result:  0\n",
      "correlation:  0.5418377777703515\n",
      "====Most recurrent class: ==== 0\n",
      "====Average correlation: ===== 0.6005015259260927\n"
     ]
    }
   ],
   "source": [
    "epochs.pick_types(eeg=True)\n",
    "X1 = epochs['6Hz'].get_data()\n",
    "list_freqs = [6, 10, 15]\n",
    "fs = 250\n",
    "num_harms = 2\n",
    "num_fbs = 3\n",
    "\n",
    "print(\"6hz: =============================================\")\n",
    "fbcca(X1, list_freqs, fs, num_harms, num_fbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10hz: ============================================\n",
      "rho:  [0.27389784 0.38697019 0.54021541]\n",
      "result:  2\n",
      "correlation:  0.5402154063334623\n",
      "rho:  [0.24763057 0.69182858 0.47600221]\n",
      "result:  1\n",
      "correlation:  0.6918285762120404\n",
      "rho:  [0.3214901  0.72233308 0.56632934]\n",
      "result:  1\n",
      "correlation:  0.7223330836229409\n",
      "rho:  [0.24155354 0.822824   0.57636536]\n",
      "result:  1\n",
      "correlation:  0.822824002546619\n",
      "rho:  [0.2760871  0.78861588 0.59215937]\n",
      "result:  1\n",
      "correlation:  0.7886158778797348\n",
      "rho:  [0.29513864 0.6067264  0.71938128]\n",
      "result:  2\n",
      "correlation:  0.7193812835697987\n",
      "rho:  [0.2659797  0.83319776 0.82603717]\n",
      "result:  1\n",
      "correlation:  0.8331977631206164\n",
      "rho:  [0.39473337 0.79742097 0.73616672]\n",
      "result:  1\n",
      "correlation:  0.7974209690435037\n",
      "rho:  [0.27742631 0.82881228 0.53083094]\n",
      "result:  1\n",
      "correlation:  0.8288122797359171\n",
      "rho:  [0.25995435 0.90003262 0.36876037]\n",
      "result:  1\n",
      "correlation:  0.9000326226714541\n",
      "rho:  [0.28066453 0.78811473 0.54326428]\n",
      "result:  1\n",
      "correlation:  0.7881147313815834\n",
      "rho:  [0.13797428 0.7240168  0.76323804]\n",
      "result:  2\n",
      "correlation:  0.7632380441673463\n",
      "rho:  [0.33028409 0.85534154 0.66050354]\n",
      "result:  1\n",
      "correlation:  0.8553415373573892\n",
      "rho:  [0.25818227 0.95851746 0.47697101]\n",
      "result:  1\n",
      "correlation:  0.9585174553368604\n",
      "rho:  [0.28804203 0.94624583 0.50408515]\n",
      "result:  1\n",
      "correlation:  0.9462458321197372\n",
      "rho:  [0.39551273 0.91831099 0.66224287]\n",
      "result:  1\n",
      "correlation:  0.9183109856375988\n",
      "rho:  [0.26108013 0.82912679 0.62044161]\n",
      "result:  1\n",
      "correlation:  0.8291267890036573\n",
      "rho:  [0.25571797 0.95611797 0.57075994]\n",
      "result:  1\n",
      "correlation:  0.9561179744205095\n",
      "rho:  [0.33809347 1.01323842 0.574322  ]\n",
      "result:  1\n",
      "correlation:  1.013238422619077\n",
      "rho:  [0.24866084 0.94225081 0.53474879]\n",
      "result:  1\n",
      "correlation:  0.9422508102313708\n",
      "====Most recurrent class: ==== 1\n",
      "====Average correlation: ===== 0.8307582223505608\n"
     ]
    }
   ],
   "source": [
    "X2 = epochs['10Hz'].get_data()\n",
    "print(\"10hz: ============================================\")\n",
    "fbcca(X2, list_freqs, fs, num_harms, num_fbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15hz: ============================================\n",
      "rho:  [0.35933473 0.45682571 0.98039248]\n",
      "result:  2\n",
      "correlation:  0.9803924761690601\n",
      "rho:  [0.35818645 0.497325   0.68968471]\n",
      "result:  2\n",
      "correlation:  0.6896847144595264\n",
      "rho:  [0.21685489 0.4642724  0.87216735]\n",
      "result:  2\n",
      "correlation:  0.872167350521599\n",
      "rho:  [0.26485271 0.43032939 1.0097039 ]\n",
      "result:  2\n",
      "correlation:  1.0097039015616343\n",
      "rho:  [0.30570961 0.62145178 0.86032861]\n",
      "result:  2\n",
      "correlation:  0.8603286116546327\n",
      "rho:  [0.3151057  0.48955774 1.00052901]\n",
      "result:  2\n",
      "correlation:  1.0005290089813326\n",
      "rho:  [0.37629926 0.58318651 1.00534427]\n",
      "result:  2\n",
      "correlation:  1.0053442689881118\n",
      "rho:  [0.19842089 0.40447288 1.10051767]\n",
      "result:  2\n",
      "correlation:  1.100517667767527\n",
      "rho:  [0.23714462 0.41493763 1.19415702]\n",
      "result:  2\n",
      "correlation:  1.1941570179695726\n",
      "rho:  [0.35095736 0.45725111 1.36718694]\n",
      "result:  2\n",
      "correlation:  1.3671869359460103\n",
      "rho:  [0.3299844  0.40493754 1.15171862]\n",
      "result:  2\n",
      "correlation:  1.151718618620872\n",
      "rho:  [0.24771264 0.34838776 1.18617321]\n",
      "result:  2\n",
      "correlation:  1.1861732123344564\n",
      "rho:  [0.19852824 0.39551462 0.95730988]\n",
      "result:  2\n",
      "correlation:  0.9573098804252202\n",
      "rho:  [0.25019735 0.4898721  1.06970085]\n",
      "result:  2\n",
      "correlation:  1.0697008494227302\n",
      "rho:  [0.32437395 0.40967831 1.00372795]\n",
      "result:  2\n",
      "correlation:  1.0037279509053707\n",
      "rho:  [0.37961386 0.64321455 0.73042838]\n",
      "result:  2\n",
      "correlation:  0.7304283840198573\n",
      "rho:  [0.22144774 0.3744802  0.88068295]\n",
      "result:  2\n",
      "correlation:  0.8806829495396779\n",
      "rho:  [0.31697144 0.41253344 0.97153384]\n",
      "result:  2\n",
      "correlation:  0.9715338408506403\n",
      "rho:  [0.31042033 0.45694726 1.12661298]\n",
      "result:  2\n",
      "correlation:  1.126612984759732\n",
      "rho:  [0.28568547 0.48850595 1.20394107]\n",
      "result:  2\n",
      "correlation:  1.2039410679845979\n",
      "====Most recurrent class: ==== 2\n",
      "====Average correlation: ===== 1.0180920846441082\n"
     ]
    }
   ],
   "source": [
    "X3 = epochs['15Hz'].get_data()\n",
    "print(\"15hz: ============================================\")\n",
    "fbcca(X3, list_freqs, fs, num_harms, num_fbs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, CCA is very powerful in categorizing the SSVEP signal."
   ]
  }
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