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| a | b/notebooks/DeepEEG_Sim.ipynb | ||
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| 1 | { |
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| 2 | "nbformat": 4, |
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| 3 | "nbformat_minor": 0, |
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| 4 | "metadata": { |
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| 5 | "colab": { |
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| 6 | "name": "DeepEEG_Sim.ipynb", |
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| 7 | "version": "0.3.2", |
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| 8 | "provenance": [], |
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| 9 | "include_colab_link": true |
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| 10 | }, |
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| 11 | "kernelspec": { |
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| 12 | "name": "python3", |
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| 13 | "display_name": "Python 3" |
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| 14 | }, |
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| 15 | "accelerator": "GPU" |
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| 16 | }, |
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| 17 | "cells": [ |
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| 18 | { |
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| 19 | "cell_type": "markdown", |
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| 20 | "metadata": { |
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| 21 | "id": "view-in-github", |
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| 22 | "colab_type": "text" |
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| 23 | }, |
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| 24 | "source": [ |
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| 25 | "<a href=\"https://colab.research.google.com/github/kylemath/DeepEEG/blob/master/notebooks/DeepEEG_Sim.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" |
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| 26 | ] |
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| 27 | }, |
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| 28 | { |
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| 29 | "cell_type": "markdown", |
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| 30 | "metadata": { |
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| 31 | "id": "cH7KRd8ZZPMd", |
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| 32 | "colab_type": "text" |
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| 33 | }, |
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| 34 | "source": [ |
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| 35 | "## DeepEEG\n" |
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| 36 | ] |
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| 37 | }, |
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| 38 | { |
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| 39 | "cell_type": "code", |
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| 40 | "metadata": { |
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| 41 | "id": "KjZu4dFMFHJV", |
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| 42 | "colab_type": "code", |
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| 43 | "colab": {} |
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| 44 | }, |
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| 45 | "source": [ |
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| 46 | "!git clone https://github.com/kylemath/DeepEEG\n", |
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| 47 | "!chmod +x ./DeepEEG/install.sh\n", |
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| 48 | "%cd DeepEEG\n", |
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| 49 | "!./install.sh\n", |
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| 50 | "from utils import *\n", |
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| 51 | "%matplotlib inline\n", |
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| 52 | "%cd .." |
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| 53 | ], |
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| 54 | "execution_count": 0, |
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| 55 | "outputs": [] |
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| 56 | }, |
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| 57 | { |
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| 58 | "cell_type": "markdown", |
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| 59 | "metadata": { |
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| 60 | "id": "FhkDPfIq1ewa", |
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| 61 | "colab_type": "text" |
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| 62 | }, |
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| 63 | "source": [ |
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| 64 | "#Simulate Data" |
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| 65 | ] |
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| 66 | }, |
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| 67 | { |
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| 68 | "cell_type": "code", |
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| 69 | "metadata": { |
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| 70 | "id": "jhU8hlcg1e6T", |
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| 71 | "colab_type": "code", |
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| 72 | "colab": {} |
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| 73 | }, |
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| 74 | "source": [ |
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| 75 | "raw,event_id = SimulateRaw(amp1 = 50, amp2 = 5, freq = 2, batch = 2)" |
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| 76 | ], |
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| 77 | "execution_count": 0, |
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| 78 | "outputs": [] |
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| 79 | }, |
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| 80 | { |
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| 81 | "cell_type": "markdown", |
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| 82 | "metadata": { |
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| 83 | "id": "AVBtNVebDtUc", |
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| 84 | "colab_type": "text" |
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| 85 | }, |
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| 86 | "source": [ |
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| 87 | "#Run Preprocessing\n", |
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| 88 | "\n", |
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| 89 | "**Input: mne.raw, event_id**\n", |
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| 90 | "\n", |
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| 91 | "**Output: mne.epochs**\n", |
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| 92 | "```python\n", |
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| 93 | "plot_psd=False\n", |
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| 94 | "filter_data=True\n", |
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| 95 | "eeg_filter_highpass=1\n", |
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| 96 | "plot_events=False\n", |
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| 97 | "epoch_time=(-.2,1)\n", |
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| 98 | "baseline=(-.2,0)\n", |
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| 99 | "rej_thresh_uV=200\n", |
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| 100 | "rereference=False\n", |
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| 101 | "emcp_raw=False\n", |
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| 102 | "emcp_epochs=False\n", |
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| 103 | "epoch_decim=1\n", |
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| 104 | "plot_electrodes=False\n", |
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| 105 | "plot_erp=False\n", |
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| 106 | "```\n", |
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| 107 | "\n", |
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| 108 | "\n" |
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| 109 | ] |
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| 110 | }, |
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| 111 | { |
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| 112 | "cell_type": "code", |
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| 113 | "metadata": { |
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| 114 | "id": "RLIKMk6P453f", |
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| 115 | "colab_type": "code", |
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| 116 | "colab": {} |
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| 117 | }, |
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| 118 | "source": [ |
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| 119 | "epochs = PreProcess(raw, event_id,filter_data=False,epoch_time = (-.2,1),plot_erp=True) \n" |
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| 120 | ], |
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| 121 | "execution_count": 0, |
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| 122 | "outputs": [] |
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| 123 | }, |
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| 124 | { |
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| 125 | "cell_type": "markdown", |
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| 126 | "metadata": { |
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| 127 | "id": "iceVBB8vFxcr", |
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| 128 | "colab_type": "text" |
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| 129 | }, |
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| 130 | "source": [ |
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| 131 | "#Plot Features:\n" |
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| 132 | ] |
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| 133 | }, |
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| 134 | { |
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| 135 | "cell_type": "code", |
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| 136 | "metadata": { |
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| 137 | "id": "LqA4ZXaPFxkC", |
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| 138 | "colab_type": "code", |
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| 139 | "colab": {} |
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| 140 | }, |
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| 141 | "source": [ |
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| 142 | "pick = 33 \n", |
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| 143 | "#select electrode\n", |
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| 144 | "for event in event_id.keys():\n", |
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| 145 | " fig = plt.imshow(epochs[event]._data[:,pick,:])\n", |
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| 146 | " plt.show()" |
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| 147 | ], |
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| 148 | "execution_count": 0, |
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| 149 | "outputs": [] |
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| 150 | }, |
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| 151 | { |
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| 152 | "cell_type": "markdown", |
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| 153 | "metadata": { |
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| 154 | "id": "pwqD_voiDypa", |
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| 155 | "colab_type": "text" |
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| 156 | }, |
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| 157 | "source": [ |
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| 158 | "#Run FeatureEngineer\n", |
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| 159 | "**Input: mne.epochs**\n", |
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| 160 | "\n", |
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| 161 | "**Output: deepeeg.feats**\n", |
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| 162 | "```python\n", |
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| 163 | "model_type='NN'\n", |
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| 164 | "frequency_domain=False\n", |
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| 165 | "normalization=True\n", |
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| 166 | "electrode_median=False\n", |
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| 167 | "wavelet_decim=1\n", |
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| 168 | "flims=(3,30)\n", |
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| 169 | "f_bins=20\n", |
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| 170 | "wave_cycles=3\n", |
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| 171 | "spect_baseline=[-1,-.5]\n", |
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| 172 | "electrodes_out=[11,12,13,14,15]\n", |
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| 173 | "test_split = 0.2\n", |
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| 174 | "val_split = 0.2\n", |
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| 175 | "random_seed=1017\n", |
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| 176 | "watermark = False\n", |
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| 177 | "```" |
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| 178 | ] |
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| 179 | }, |
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| 180 | { |
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| 181 | "cell_type": "code", |
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| 182 | "metadata": { |
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| 183 | "id": "i_jYlTW1A6sb", |
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| 184 | "colab_type": "code", |
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| 185 | "colab": {} |
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| 186 | }, |
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| 187 | "source": [ |
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| 188 | "feats = FeatureEngineer(epochs, model_type = 'NN',\n", |
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| 189 | " frequency_domain=True, \n", |
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| 190 | " normalization= False,\n", |
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| 191 | " flims=(5,20),spect_baseline=[-.5,0]\n", |
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| 192 | " )" |
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| 193 | ], |
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| 194 | "execution_count": 0, |
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| 195 | "outputs": [] |
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| 196 | }, |
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| 197 | { |
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| 198 | "cell_type": "markdown", |
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| 199 | "metadata": { |
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| 200 | "id": "9ChMvr-jKp8P", |
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| 201 | "colab_type": "text" |
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| 202 | }, |
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| 203 | "source": [ |
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| 204 | "# Run CreateModel\n", |
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| 205 | "\n", |
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| 206 | "**Input: deepeeg.feats**\n", |
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| 207 | "\n", |
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| 208 | "**Output: deepeeg.model, deepeeg.encoder**\n", |
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| 209 | "\n", |
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| 210 | "```python\n", |
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| 211 | "units=[16,8,4,8,16]\n", |
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| 212 | "dropout=.25\n", |
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| 213 | "batch_norm=True\n", |
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| 214 | "filt_size=3\n", |
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| 215 | "pool_size=2\n", |
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| 216 | "```" |
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| 217 | ] |
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| 218 | }, |
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| 219 | { |
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| 220 | "cell_type": "code", |
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| 221 | "metadata": { |
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| 222 | "id": "HFf3rBbJKqHR", |
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| 223 | "colab_type": "code", |
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| 224 | "colab": {} |
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| 225 | }, |
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| 226 | "source": [ |
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| 227 | "model, _ = CreateModel(feats, units=[512,512])" |
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| 228 | ], |
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| 229 | "execution_count": 0, |
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| 230 | "outputs": [] |
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| 231 | }, |
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| 232 | { |
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| 233 | "cell_type": "markdown", |
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| 234 | "metadata": { |
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| 235 | "id": "-EKBjSapfE4O", |
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| 236 | "colab_type": "text" |
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| 237 | }, |
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| 238 | "source": [ |
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| 239 | "# TrainTestVal\n", |
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| 240 | "\n", |
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| 241 | "**Input: deepEEG.model, deepEEG.feats**\n", |
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| 242 | "\n", |
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| 243 | "```python\n", |
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| 244 | "batch_size=2\n", |
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| 245 | "train_epochs=20\n", |
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| 246 | "show_plots=True\n", |
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| 247 | "```" |
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| 248 | ] |
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| 249 | }, |
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| 250 | { |
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| 251 | "cell_type": "code", |
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| 252 | "metadata": { |
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| 253 | "id": "u6ize7eJfB3J", |
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| 254 | "colab_type": "code", |
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| 255 | "colab": {} |
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| 256 | }, |
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| 257 | "source": [ |
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| 258 | "TrainTestVal(model, feats)" |
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| 259 | ], |
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| 260 | "execution_count": 0, |
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| 261 | "outputs": [] |
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| 262 | } |
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| 263 | ] |
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| 264 | } |