{
 "cells": [
  {
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   "execution_count": 12,
   "id": "initial_id",
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     "start_time": "2024-02-29T01:56:53.225604200Z"
    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating RawArray with float64 data, n_channels=30, n_times=440\n",
      "    Range : 0 ... 439 =      0.000 ...     0.399 secs\n",
      "Ready.\n",
      "Opening raw-browser...\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import mne\n",
    "import matplotlib.pyplot as plt\n",
    "data = np.load(\"ABMD_results3.npy\", allow_pickle=True).item()\n",
    "# 获取data字典中的eeg键的第一个元素\n",
    "sample = data['data'][0]\n",
    "# 创建一个列表，其中包含30个eeg1到eeg30的名称\n",
    "channels = [f\"eeg{i}\" for i in range(1, 31)]\n",
    "# 创建一个列表，其中包含30个eeg类型的元素\n",
    "channel_types = [\"eeg\"] * 30\n",
    "# 使用mne模块创建一个info对象，其中包含上面创建的channels和channel_types列表\n",
    "info = mne.create_info(ch_names=channels,\n",
    "                       sfreq=1100,\n",
    "                       ch_types=channel_types)\n",
    "# 使用mne模块创建一个RawArray对象，其中包含sample和info对象\n",
    "raw = mne.io.RawArray(sample, info)\n",
    "fig = raw.plot(start=0, duration=10, color='b', scalings='auto')\n",
    "# plt.show()\n",
    "# plt.savefig(\"ABMD_results3.png\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T01:48:22.905570700Z",
     "start_time": "2024-02-29T01:48:22.873578500Z"
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   "id": "4805d7e6a7da9fea"
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  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "242d359be6dc5d61"
  }
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