notebooks/Deep_EEG_Muse.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Deep_EEG_Muse.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kylemath/DeepEEG/blob/master/notebooks/Deep_EEG_Muse.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cH7KRd8ZZPMd",
        "colab_type": "text"
      },
      "source": [
        "## DeepEEG\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjZu4dFMFHJV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "c6c06cec-2bcb-49b4-ddae-32e16b5da89d"
      },
      "source": [
        "!git clone https://github.com/kylemath/DeepEEG\n",
        "!chmod +x ./DeepEEG/install.sh\n",
        "%cd DeepEEG\n",
        "!./install.sh\n",
        "from utils import *\n",
        "%matplotlib inline"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'DeepEEG'...\n",
            "remote: Enumerating objects: 13, done.\u001b[K\n",
            "remote: Counting objects: 100% (13/13), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
            "remote: Total 947 (delta 2), reused 5 (delta 1), pack-reused 934\u001b[K\n",
            "Receiving objects: 100% (947/947), 19.62 MiB | 8.43 MiB/s, done.\n",
            "Resolving deltas: 100% (489/489), done.\n",
            "/content/DeepEEG/DeepEEG\n",
            "Requirement already up-to-date: pip in /usr/local/lib/python3.6/dist-packages (20.2.2)\n",
            "Requirement already satisfied: absl-py==0.7.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (0.7.0)\n",
            "Requirement already satisfied: astor==0.7.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 2)) (0.7.1)\n",
            "Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (0.10.0)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (0.2.2)\n",
            "Requirement already satisfied: grpcio==1.18.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (1.18.0)\n",
            "Requirement already satisfied: h5py==2.9.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 6)) (2.9.0)\n",
            "Requirement already satisfied: Keras==2.2.4 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 7)) (2.2.4)\n",
            "Requirement already satisfied: Keras-Applications==1.0.7 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 8)) (1.0.7)\n",
            "Requirement already satisfied: Keras-Preprocessing==1.0.9 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 9)) (1.0.9)\n",
            "Requirement already satisfied: kiwisolver==1.0.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 10)) (1.0.1)\n",
            "Requirement already satisfied: Markdown==3.0.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 11)) (3.0.1)\n",
            "Requirement already satisfied: matplotlib==3.0.2 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 12)) (3.0.2)\n",
            "Requirement already satisfied: mne==0.17.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 13)) (0.17.0)\n",
            "Requirement already satisfied: numpy==1.16.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 14)) (1.16.1)\n",
            "Requirement already satisfied: pandas==0.24.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 15)) (0.24.1)\n",
            "Requirement already satisfied: protobuf==3.6.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 16)) (3.6.1)\n",
            "Requirement already satisfied: pyparsing==2.3.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 17)) (2.3.1)\n",
            "Requirement already satisfied: python-dateutil==2.8.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 18)) (2.8.0)\n",
            "Requirement already satisfied: pytz==2018.9 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 19)) (2018.9)\n",
            "Requirement already satisfied: PyYAML==4.2b1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 20)) (4.2b1)\n",
            "Requirement already satisfied: scikit-learn==0.20.2 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 21)) (0.20.2)\n",
            "Requirement already satisfied: scipy==1.2.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 22)) (1.2.0)\n",
            "Requirement already satisfied: six==1.12.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 23)) (1.12.0)\n",
            "Requirement already satisfied: sklearn==0.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 24)) (0.0)\n",
            "Requirement already satisfied: tensorboard==1.12.2 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 25)) (1.12.2)\n",
            "Requirement already satisfied: tensorflow==1.13.0rc1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 26)) (1.13.0rc1)\n",
            "Requirement already satisfied: termcolor==1.1.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 27)) (1.1.0)\n",
            "Requirement already satisfied: Werkzeug==0.14.1 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 28)) (0.14.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver==1.0.1->-r requirements.txt (line 10)) (49.6.0)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorboard==1.12.2->-r requirements.txt (line 25)) (0.35.1)\n",
            "Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.0rc1->-r requirements.txt (line 26)) (1.13.0)\n",
            "Requirement already satisfied: mock>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0rc0->tensorflow==1.13.0rc1->-r requirements.txt (line 26)) (4.0.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhkDPfIq1ewa",
        "colab_type": "text"
      },
      "source": [
        "#Load Data, Concatenate\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhU8hlcg1e6T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f5548962-7f6e-4dbb-83f4-618cf9c36f49"
      },
      "source": [
        "!git clone https://github.com/kylemath/eeg-notebooks_v0.1/\n",
        "data_dir = 'visual/cueing'\n",
        "\n",
        "subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,\n",
        "        202, 203, 204, 205, 207, 208, 209, 210, 211,\n",
        "        301, 302, 303, 304, 305, 306, 307, 308, 309,\n",
        "        1101, 1102, 1103, 1104, 1105, 1106, 1108, 1109, 1110,\n",
        "        1202, 1203, 1205, 1206, 1209, 1210, 1211, 1215,\n",
        "        1301, 1302, 1313, \n",
        "        1401, 1402, 1403, 1404, 1405,  1408, 1410, 1411, 1412, 1413, 1413, 1414, 1415, 1416]\n",
        "\n",
        "nsesh = 2\n",
        "event_id = {'LeftCue':1, 'RightCue':2}\n",
        "\n",
        "raw = LoadMuseData(subs,nsesh,data_dir)\n",
        "\n",
        "#subs = [ 1]\n",
        "#nsesh = 1\n",
        "#data_dir = 'visual/P300'\n",
        "#event_names = ['Standard','Target']\n",
        "\n",
        "#subs = [ 4]\n",
        "#nsesh = 1\n",
        "#data_dir = 'visual/SSVEP'\n",
        "#event_names = ['f30Hz','f20Hz']"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'eeg-notebooks_v0.1'...\n",
            "remote: Enumerating objects: 2451, done.\u001b[K\n",
            "remote: Total 2451 (delta 0), reused 0 (delta 0), pack-reused 2451\u001b[K\n",
            "Receiving objects: 100% (2451/2451), 166.74 MiB | 12.34 MiB/s, done.\n",
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            "Loading Data\n",
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        {
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          "ename": "TypeError",
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          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-6099eec806a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mevent_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'LeftCue'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'RightCue'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mraw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLoadMuseData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnsesh\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m#subs = [ 1]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/DeepEEG/utils.py\u001b[0m in \u001b[0;36mLoadMuseData\u001b[0;34m(subs, nsesh, data_dir, load_verbose, sfreq)\u001b[0m\n\u001b[1;32m    106\u001b[0m       raw.append(muse_load_data(data_dir, sfreq=sfreq ,subject_nb=sub,\n\u001b[1;32m    107\u001b[0m                     session_nb=isesh+1,verbose=load_verbose))\n\u001b[0;32m--> 108\u001b[0;31m   \u001b[0mraw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcatenate_raws\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/mne/io/base.py\u001b[0m in \u001b[0;36mconcatenate_raws\u001b[0;34m(raws, preload, events_list, verbose)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/mne/utils.py\u001b[0m in \u001b[0;36mverbose\u001b[0;34m(function, *args, **kwargs)\u001b[0m\n\u001b[1;32m    950\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0muse_log_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbose_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    951\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 952\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/mne/io/base.py\u001b[0m in \u001b[0;36mconcatenate_raws\u001b[0;34m(raws, preload, events_list, verbose)\u001b[0m\n\u001b[1;32m   2635\u001b[0m         \u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst_samp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_samp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mraws\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2636\u001b[0m         \u001b[0mevents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcatenate_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevents_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2637\u001b[0;31m     \u001b[0mraws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreload\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2639\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mevents_list\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: append() takes exactly one argument (2 given)"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEd72sIHuiFc",
        "colab_type": "text"
      },
      "source": [
        "#Run Preprocessing\n",
        "\n",
        "**Input:  mne.raw, event_id**\n",
        "\n",
        "**Output: mne.epochs**\n",
        "```python\n",
        "plot_psd=False\n",
        "filter_data=True\n",
        "eeg_filter_highpass=1\n",
        "plot_events=False\n",
        "epoch_time=(-.2,1)\n",
        "baseline=(-.2,0)\n",
        "rej_thresh_uV=200\n",
        "rereference=False\n",
        "emcp_raw=False\n",
        "emcp_epochs=False\n",
        "epoch_decim=1\n",
        "plot_electrodes=False\n",
        "plot_erp=False\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HiHs8CluhjP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "70c40caf-689c-4f7e-868d-7b94affceeb7"
      },
      "source": [
        "epochs = PreProcess(raw,event_id)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Filtering Data Between 1 and 30 Hz.\n",
            "4583 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Remaining Trials: 1408\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwqD_voiDypa",
        "colab_type": "text"
      },
      "source": [
        "#Run FeatureEngineer\n",
        "**Input:  mne.epochs**\n",
        "\n",
        "**Output: deepeeg.feats**\n",
        "```python\n",
        "model_type='NN'\n",
        "frequency_domain=False\n",
        "normalization=True\n",
        "electrode_median=False\n",
        "wavelet_decim=1\n",
        "flims=(3,30)\n",
        "f_bins=20\n",
        "wave_cycles=3\n",
        "spect_baseline=[-1,-.5]\n",
        "electrodes_out=[11,12,13,14,15]\n",
        "test_split = 0.2\n",
        "val_split = 0.2\n",
        "random_seed=1017\n",
        "watermark = False\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_jYlTW1A6sb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "4a93d84c-7ea9-45a9-d312-f801fb29fcb1"
      },
      "source": [
        "feats = FeatureEngineer(epochs,model_type='NN')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Constructing Time Domain Features\n",
            "Normalizing X\n",
            "Combined X Shape: (1408, 256, 4)\n",
            "Combined Y Shape: (1408,)\n",
            "Y Example (should be 1s & 0s): [1 1 0 0 0 0 0 1 0 0]\n",
            "X Range: -12.106191:9.781672\n",
            "Input Shape: (256, 4)\n",
            "x_train shape: (844, 256, 4)\n",
            "844 train samples\n",
            "282 test samples\n",
            "282 validation samples\n",
            "Class Weights: [0.97011494 1.03178484]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ChMvr-jKp8P",
        "colab_type": "text"
      },
      "source": [
        "# Run CreateModel\n",
        "\n",
        "**Input: deepeeg.feats**\n",
        "\n",
        "**Output: deepeeg.model, deepeeg.encoder**\n",
        "\n",
        "```python\n",
        "units=[16,8,4,8,16]\n",
        "dropout=.25\n",
        "batch_norm=True\n",
        "filt_size=3\n",
        "pool_size=2\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFf3rBbJKqHR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model, _ = CreateModel(feats)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EKBjSapfE4O",
        "colab_type": "text"
      },
      "source": [
        "# TrainTestVal\n",
        "\n",
        "**Input: deepEEG.model, deepEEG.feats**\n",
        "\n",
        "```python\n",
        "batch_size=2\n",
        "train_epochs=20\n",
        "show_plots=True\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6ize7eJfB3J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TrainTestVal(model, feats)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}