export_fif_with_events.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自己设置event事件，并导出为带该事件的fif文件\n",
    "# https://mne.tools/stable/auto_tutorials/raw/plot_30_annotate_raw.html#sphx-glr-auto-tutorials-raw-plot-30-annotate-raw-py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from mne.io import read_raw_fif\n",
    "import mne\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening raw data file /Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss.fif...\n",
      "    Range : 17000 ... 496999 =     17.000 ...   496.999 secs\n",
      "Ready.\n",
      "Reading 0 ... 479999  =      0.000 ...   479.999 secs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a8bec9b7c3ae>:1: RuntimeWarning: This filename (/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz or _meg.fif\n",
      "  raw = read_raw_fif(\"/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss.fif\",preload=True)\n"
     ]
    }
   ],
   "source": [
    "raw = read_raw_fif(\"/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss.fif\",preload=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# raw.info['ch_names'] \n",
    "\n",
    "#  'STI101',\n",
    "#  'STI201',\n",
    "#  'STI301']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "events = pd.read_csv(\"/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_MarkerFile-bst.txt\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>spike</td>\n",
       "      <td>31.926</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spike</td>\n",
       "      <td>47.610</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spike</td>\n",
       "      <td>55.081</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spike</td>\n",
       "      <td>70.258</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spike</td>\n",
       "      <td>73.494</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spike</td>\n",
       "      <td>79.761</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>spike</td>\n",
       "      <td>80.386</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>spike</td>\n",
       "      <td>85.830</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spike</td>\n",
       "      <td>94.389</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spike</td>\n",
       "      <td>134.203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>spike</td>\n",
       "      <td>140.992</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>spike</td>\n",
       "      <td>144.688</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>spike</td>\n",
       "      <td>147.296</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>spike</td>\n",
       "      <td>167.947</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>spike</td>\n",
       "      <td>172.314</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>spike</td>\n",
       "      <td>216.726</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spike</td>\n",
       "      <td>225.676</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>spike</td>\n",
       "      <td>244.143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>spike</td>\n",
       "      <td>288.719</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>spike</td>\n",
       "      <td>303.938</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>spike</td>\n",
       "      <td>405.255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0        1  2\n",
       "0   spike   31.926  0\n",
       "1   spike   47.610  0\n",
       "2   spike   55.081  0\n",
       "3   spike   70.258  0\n",
       "4   spike   73.494  0\n",
       "5   spike   79.761  0\n",
       "6   spike   80.386  0\n",
       "7   spike   85.830  0\n",
       "8   spike   94.389  0\n",
       "9   spike  134.203  0\n",
       "10  spike  140.992  0\n",
       "11  spike  144.688  0\n",
       "12  spike  147.296  0\n",
       "13  spike  167.947  0\n",
       "14  spike  172.314  0\n",
       "15  spike  216.726  0\n",
       "16  spike  225.676  0\n",
       "17  spike  244.143  0\n",
       "18  spike  288.719  0\n",
       "19  spike  303.938  0\n",
       "20  spike  405.255  0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标记的spike事件 \n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfreq = raw.info['sfreq']\n",
    "sfreq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "onset: [ 31926  47610  55081  70258  73494  79761  80386  85830  94389 134203\n",
      " 140992 144688 147296 167947 172314 216726 225676 244143 288719 303938\n",
      " 405255]\n",
      "spike_event_id: [10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001, 10001]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(31926, 0, 10001),\n",
       " (47610, 0, 10001),\n",
       " (55081, 0, 10001),\n",
       " (70258, 0, 10001),\n",
       " (73494, 0, 10001),\n",
       " (79761, 0, 10001),\n",
       " (80386, 0, 10001),\n",
       " (85830, 0, 10001),\n",
       " (94389, 0, 10001),\n",
       " (134203, 0, 10001),\n",
       " (140992, 0, 10001),\n",
       " (144688, 0, 10001),\n",
       " (147296, 0, 10001),\n",
       " (167947, 0, 10001),\n",
       " (172314, 0, 10001),\n",
       " (216726, 0, 10001),\n",
       " (225676, 0, 10001),\n",
       " (244143, 0, 10001),\n",
       " (288719, 0, 10001),\n",
       " (303938, 0, 10001),\n",
       " (405255, 0, 10001)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert event for mne\n",
    "# (onset,pre event_id,event_id) as. int\n",
    "\n",
    "mne_events = [[]]\n",
    "# mne_events[:,] \n",
    "\n",
    "onset = events.iloc[:,1].to_numpy() * sfreq\n",
    "onset = onset.astype(int)\n",
    "spike_event_id = [10001] * len(onset)\n",
    "pre_event_id = [0] * len(onset)\n",
    "print(\"onset:\",onset)\n",
    "\n",
    "print(\"spike_event_id:\",spike_event_id)\n",
    "\n",
    "mne_events = list(zip(onset,pre_event_id,spike_event_id))\n",
    "mne_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# method1: add spike event\n",
    "# https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.add_events\n",
    "raw.add_events(mne_events,'STI101')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting existing file.\n",
      "Writing /Volumes/R/MEG/sample_meg_raw_spike.fif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-84-07e2bce10eb7>:1: RuntimeWarning: This filename (/Volumes/R/MEG/sample_meg_raw_spike.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz or _meg.fif\n",
      "  raw.save('/Volumes/R/MEG/sample_meg_raw_spike.fif',overwrite=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing /Volumes/R/MEG/sample_meg_raw_spike.fif\n",
      "[done]\n"
     ]
    }
   ],
   "source": [
    "raw.save('/Volumes/R/MEG/sample_meg_raw_spike.fif',overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening raw data file /Volumes/R/MEG/sample_meg_raw_spike.fif...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-85-c03e8cad4e04>:1: RuntimeWarning: This filename (/Volumes/R/MEG/sample_meg_raw_spike.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz or _meg.fif\n",
      "  raw_eve = read_raw_fif(\"/Volumes/R/MEG/sample_meg_raw_spike.fif\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Range : 17000 ... 496999 =     17.000 ...   496.999 secs\n",
      "Ready.\n"
     ]
    }
   ],
   "source": [
    "raw_eve = read_raw_fif(\"/Volumes/R/MEG/sample_meg_raw_spike.fif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 events found\n",
      "Event IDs: [10001]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 31926,      0,  10001],\n",
       "       [ 47610,      0,  10001],\n",
       "       [ 55081,      0,  10001],\n",
       "       [ 73494,      0,  10001],\n",
       "       [ 94389,      0,  10001],\n",
       "       [134203,      0,  10001]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mne.find_events(raw_eve, stim_channel='STI101')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(31926, 0, 10001),\n",
       " (47610, 0, 10001),\n",
       " (55081, 0, 10001),\n",
       " (73494, 0, 10001),\n",
       " (94389, 0, 10001),\n",
       " (134203, 0, 10001)]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mne_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2017, 3, 2, 3, 14, 24, 980120, tzinfo=datetime.timezone.utc)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method2: add spike annotation\n",
    "# https://mne.tools/stable/auto_tutorials/raw/plot_30_annotate_raw.html#sphx-glr-auto-tutorials-raw-plot-30-annotate-raw-py\n",
    "# “”“\n",
    "# Notice that orig_time is None, because we haven’t specified it. In those cases, when you add the annotations to a Raw object, \n",
    "# it is assumed that the orig_time matches the time of the first sample of the recording, so orig_time will be set to\n",
    "# match the recording measurement date (raw.info['meas_date']).\n",
    "# ”“”\n",
    "raw.info['meas_date']\n",
    "orig_time = raw.annotations.orig_time\n",
    "orig_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meas_date: 2017-03-02 03:14:24.980120+00:00\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "meas_date = raw.info['meas_date']\n",
    "print(\"meas_date:\",meas_date)\n",
    "orig_time = raw.annotations.orig_time\n",
    "print(meas_date == orig_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 找到数据记录的onset，即在记录之前，系统已经开始计数。\n",
    "# Since the example data comes from a Neuromag system that starts counting sample numbers before the recording begins,\n",
    "time_of_first_sample = raw.first_samp / raw.info['sfreq']\n",
    "time_of_first_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-03-02 03:14:24.980120\n"
     ]
    }
   ],
   "source": [
    "time_format = '%Y-%m-%d %H:%M:%S.%f'\n",
    "new_orig_time = (meas_date + timedelta(seconds=0)).strftime(time_format)\n",
    "print(new_orig_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "onsets: [ 31.776  47.46   54.931  70.108  73.344  79.611  80.236  85.68   94.239\n",
      " 134.053 140.842 144.538 147.146 167.797 172.164 216.576 225.526 243.993\n",
      " 288.569 303.788 405.105]\n",
      "<Annotations | 21 segments: spike (21)>\n",
      "[ 31.776  47.46   54.931  70.108  73.344  79.611  80.236  85.68   94.239\n",
      " 134.053 140.842 144.538 147.146 167.797 172.164 216.576 225.526 243.993\n",
      " 288.569 303.788 405.105]\n",
      "[ 31.776  47.46   54.931  70.108  73.344  79.611  80.236  85.68   94.239\n",
      " 134.053 140.842 144.538 147.146 167.797 172.164 216.576 225.526 243.993\n",
      " 288.569 303.788 405.105]\n"
     ]
    }
   ],
   "source": [
    "# later_annot = mne.Annotations(onset=[3, 5, 7],\n",
    "#                               duration=[1, 0.5, 0.25],\n",
    "#                               description=['DDD', 'EEE', 'FFF'],\n",
    "#                               orig_time=new_orig_time)\n",
    "\n",
    "onsets = (onset - 150)/sfreq\n",
    "duration = [0.3] * len(onset)\n",
    "print(\"onsets:\",onsets)\n",
    "spike_event_name = ['spike'] * len(onset)\n",
    "spike_annot = mne.Annotations(onset=onsets,\n",
    "                           duration=duration,\n",
    "                           description=spike_event_name,\n",
    "                           orig_time=new_orig_time)\n",
    "print(spike_annot)\n",
    "raw2 = raw.copy().set_annotations(spike_annot)\n",
    "print(spike_annot.onset)\n",
    "print(raw2.annotations.onset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing /Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-bbd1fecfd0a6>:1: RuntimeWarning: This filename (/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz or _meg.fif\n",
      "  raw2.save('/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif',overwrite=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing /Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif\n",
      "[done]\n",
      "Opening raw data file /Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif...\n",
      "    Range : 17000 ... 496999 =     17.000 ...   496.999 secs\n",
      "Ready.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-bbd1fecfd0a6>:2: RuntimeWarning: This filename (/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz or _meg.fif\n",
      "  raw_eve = read_raw_fif(\"/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif\")\n"
     ]
    }
   ],
   "source": [
    "raw2.save('/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif',overwrite=True)\n",
    "raw_eve = read_raw_fif(\"/Volumes/Lexar/MEG/MEG2224_li_yi_20170302_EP_4_tsss_spike_annot.fif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 617.04x449.28 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_eve.plot(start=30, duration=6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 31.775999,  47.459999,  54.931   ,  73.344002,  94.238998,\n",
       "       134.052994])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_eve.annotations.onset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from /Users/reallo/Downloads/eeg-test/sub-001/sess/eeg/sub-001_task-P300_run-01_eeg.bdf...\n",
      "BDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 194047  =      0.000 ...   757.996 secs...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Info | 7 non-empty values\n",
       " bads: []\n",
       " ch_names: Fp1, AF7, AF3, F1, F3, F5, F7, FT7, FC5, FC3, FC1, C1, C3, C5, ...\n",
       " chs: 79 EEG, 1 STIM\n",
       " custom_ref_applied: False\n",
       " highpass: 0.0 Hz\n",
       " lowpass: 52.0 Hz\n",
       " meas_date: 2011-08-12 15:47:56 UTC\n",
       " nchan: 80\n",
       " projs: []\n",
       " sfreq: 256.0 Hz\n",
       ">"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P300 event\n",
    "eeg_raw = mne.io.read_raw_bdf(\"/Users/reallo/Downloads/eeg-test/sub-001/sess/eeg/sub-001_task-P300_run-01_eeg.bdf\",preload=True)\n",
    "eeg_raw.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Fp1',\n",
       " 'AF7',\n",
       " 'AF3',\n",
       " 'F1',\n",
       " 'F3',\n",
       " 'F5',\n",
       " 'F7',\n",
       " 'FT7',\n",
       " 'FC5',\n",
       " 'FC3',\n",
       " 'FC1',\n",
       " 'C1',\n",
       " 'C3',\n",
       " 'C5',\n",
       " 'T7',\n",
       " 'TP7',\n",
       " 'CP5',\n",
       " 'CP3',\n",
       " 'CP1',\n",
       " 'P1',\n",
       " 'P3',\n",
       " 'P5',\n",
       " 'P7',\n",
       " 'P9',\n",
       " 'PO7',\n",
       " 'PO3',\n",
       " 'O1',\n",
       " 'Iz',\n",
       " 'Oz',\n",
       " 'POz',\n",
       " 'Pz',\n",
       " 'CPz',\n",
       " 'Fpz',\n",
       " 'Fp2',\n",
       " 'AF8',\n",
       " 'AF4',\n",
       " 'AFz',\n",
       " 'Fz',\n",
       " 'F2',\n",
       " 'F4',\n",
       " 'F6',\n",
       " 'F8',\n",
       " 'FT8',\n",
       " 'FC6',\n",
       " 'FC4',\n",
       " 'FC2',\n",
       " 'FCz',\n",
       " 'Cz',\n",
       " 'C2',\n",
       " 'C4',\n",
       " 'C6',\n",
       " 'T8',\n",
       " 'TP8',\n",
       " 'CP6',\n",
       " 'CP4',\n",
       " 'CP2',\n",
       " 'P2',\n",
       " 'P4',\n",
       " 'P6',\n",
       " 'P8',\n",
       " 'P10',\n",
       " 'PO8',\n",
       " 'PO4',\n",
       " 'O2',\n",
       " 'EXG1',\n",
       " 'EXG2',\n",
       " 'EXG3',\n",
       " 'EXG4',\n",
       " 'EXG5',\n",
       " 'EXG6',\n",
       " 'EXG7',\n",
       " 'EXG8',\n",
       " 'GSR1',\n",
       " 'GSR2',\n",
       " 'Erg1',\n",
       " 'Erg2',\n",
       " 'Resp',\n",
       " 'Plet',\n",
       " 'Temp',\n",
       " 'Status']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg_raw.info['ch_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfreq = eeg_raw.info['sfreq']\n",
    "sfreq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trigger channel has a non-zero initial value of 65536 (consider using initial_event=True to detect this event)\n",
      "Removing orphaned offset at the beginning of the file.\n",
      "863 events found\n",
      "Event IDs: [1 2 3 4 8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[  1139,      0,      2],\n",
       "       [  1346,      0,      2],\n",
       "       [  1604,      0,      2],\n",
       "       ...,\n",
       "       [193483,      0,      1],\n",
       "       [193644,      0,      2],\n",
       "       [193897,      0,      2]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # extract the events array from the stim channel\n",
    "events = mne.find_events(eeg_raw)\n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.449  5.258  6.266  7.328  8.352  9.273 10.27  11.312 12.34  13.359]\n",
      "[0 0 0 0 0 0 0 0 0 0]\n",
      "[2 2 2 2 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "# 脑电数据\n",
    "evt_onset = np.around(events[:,0]/sfreq,decimals=3)\n",
    "print(evt_onset[:10])\n",
    "\n",
    "evt_duration = events[:,1]\n",
    "print(evt_duration[:10])\n",
    "\n",
    "evt_type = events[:,2]\n",
    "print(evt_type[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening raw data file /Users/reallo/Downloads/MEG2224_Li_yi_20170302_EP_1.fif...\n",
      "    Read a total of 7 projection items:\n",
      "        mag_ssp_supine.fif : PCA-v1 (1 x 306)  idle\n",
      "        mag_ssp_supine.fif : PCA-v2 (1 x 306)  idle\n",
      "        mag_ssp_supine.fif : PCA-v3 (1 x 306)  idle\n",
      "        mag_ssp_supine.fif : PCA-v4 (1 x 306)  idle\n",
      "        mag_ssp_supine.fif : PCA-v5 (1 x 306)  idle\n",
      "        grad_ssp_supine.fif : PCA-v1 (1 x 306)  idle\n",
      "        grad_ssp_supine.fif : PCA-v2 (1 x 306)  idle\n",
      "    Range : 18000 ... 501999 =     18.000 ...   501.999 secs\n",
      "Ready.\n",
      "Reading 0 ... 483999  =      0.000 ...   483.999 secs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-8123e375dfe3>:2: RuntimeWarning: This filename (/Users/reallo/Downloads/MEG2224_Li_yi_20170302_EP_1.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz or _meg.fif\n",
      "  meg_raw = mne.io.read_raw_fif(\"/Users/reallo/Downloads/MEG2224_Li_yi_20170302_EP_1.fif\",preload=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11610 events found\n",
      "Event IDs: [1 2 3 4 5 6 7 8 9]\n",
      "[0.027 0.045 0.064 0.083 0.102 0.12  0.139 0.158 0.177 0.402 0.42  0.439\n",
      " 0.458 0.477 0.496 0.514 0.533 0.552 0.777 0.796]\n",
      "[0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 0 1]\n",
      "[1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-8123e375dfe3>:5: RuntimeWarning: Trigger channel contains negative values, using absolute value. If data were acquired on a Neuromag system with STI016 active, consider using uint_cast=True to work around an acquisition bug\n",
      "  events = mne.find_events(meg_raw,stim_channel=['STI101','STI201','STI301']) # 脑磁图事件通道名称：'STI101' 'STI201' 'STI301'\n"
     ]
    }
   ],
   "source": [
    "# 脑磁数据\n",
    "meg_raw = mne.io.read_raw_fif(\"/Users/reallo/Downloads/MEG2224_Li_yi_20170302_EP_1.fif\",preload=True)\n",
    "meg_raw.info\n",
    "sfreq = meg_raw.info['sfreq']\n",
    "events = mne.find_events(meg_raw,stim_channel=['STI101','STI201','STI301']) # 脑磁图事件通道名称：'STI101' 'STI201' 'STI301'\n",
    "evt_onset = np.around((events[:, 0] - meg_raw.first_samp) / meg_raw.info['sfreq'],decimals=3) #剔除first_samp的数据（即未记录的前几秒脑磁数据被去掉）；\n",
    "print(evt_onset[:20])\n",
    "\n",
    "evt_duration = events[:,1]\n",
    "print(evt_duration[:20])\n",
    "\n",
    "evt_type = events[:,2]\n",
    "print(evt_type[:20])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 18027,  18045,  18064, ..., 501740, 501758, 501777])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=int32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evt_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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