{ "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": [ "\"Open" ] }, { "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", "Resolving deltas: 100% (1096/1096), done.\n", "Checking out files: 100% (632/632), done.\n", "Loading Data\n", "Subject number 1/63\n", " Session number 1/2\n", "No files for subject with filename []\n", " Session number 2/2\n", "No files for subject with filename []\n", "Subject number 2/63\n", " 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subject with filename []\n" ], "name": "stdout" }, { "output_type": "error", "ename": "TypeError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\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 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"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": [] } ] }