Datasets
Models
Downloads: 1
422 lines (421 with data), 15.2 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using default location ~/mne_data for EEGBCI...\n",
"Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R05.edf (2.5 MB)\n",
"[........................................] 100.00000 \\ ( 2.5 MB / 2.5 MB, 2.3 MB/s) \n",
"Do you want to set the path:\n",
" /home/fred/mne_data\n",
"as the default EEGBCI dataset path in the mne-python config [y]/n? y\n",
"Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R06.edf (2.5 MB)\n",
"[........................................] 100.00000 / ( 2.5 MB / 2.5 MB, 15.0 MB/s) \n",
"Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R09.edf (2.5 MB)\n",
"[........................................] 100.00000 \\ ( 2.5 MB / 2.5 MB, 30.2 MB/s) \n",
"Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R10.edf (2.5 MB)\n",
"[........................................] 100.00000 - ( 2.5 MB / 2.5 MB, 18.7 MB/s) \n",
"Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R13.edf (2.5 MB)\n",
"[........................................] 100.00000 \\ ( 2.5 MB / 2.5 MB, 25.3 MB/s) \n",
"Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R14.edf (2.5 MB)\n",
"[........................................] 100.00000 | ( 2.5 MB / 2.5 MB, 25.5 MB/s) \n",
"Removing orphaned offset at the beginning of the file.\n",
"179 events found\n",
"Events id: [1 2 3]\n",
"90 matching events found\n",
"Loading data for 90 events and 497 original time points ...\n",
"0 bad epochs dropped\n"
]
}
],
"source": [
"\n",
"import mne\n",
"from mne.io import concatenate_raws\n",
"\n",
"# 5,6,7,10,13,14 are codes for executed and imagined hands/feet\n",
"subject_id = 1\n",
"event_codes = [5,6,9,10,13,14]\n",
"\n",
"# This will download the files if you don't have them yet,\n",
"# and then return the paths to the files.\n",
"physionet_paths = mne.datasets.eegbci.load_data(subject_id, event_codes)\n",
"\n",
"# Load each of the files\n",
"parts = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto', verbose='WARNING')\n",
" for path in physionet_paths]\n",
"\n",
"# Concatenate them\n",
"raw = concatenate_raws(parts)\n",
"\n",
"# Find the events in this dataset\n",
"events = mne.find_events(raw, shortest_event=0, stim_channel='STI 014')\n",
"\n",
"# Use only EEG channels\n",
"eeg_channel_inds = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n",
" exclude='bads')\n",
"\n",
"# Extract trials, only using EEG channels\n",
"epoched = mne.Epochs(raw, events, dict(hands=2, feet=3), tmin=1, tmax=4.1, proj=False, picks=eeg_channel_inds,baseline=None, preload=True)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"# Convert data from volt to millivolt\n",
"# Pytorch expects float32 for input and int64 for labels.\n",
"X = (epoched.get_data() * 1e6).astype(np.float32)\n",
"y = (epoched.events[:,2] - 2).astype(np.int64) #2,3 -> 0,1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from braindecode.datautil.signal_target import SignalAndTarget\n",
"\n",
"train_set = SignalAndTarget(X[:60], y=y[:60])\n",
"test_set = SignalAndTarget(X[60:], y=y[60:])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"\n",
"from braindecode.models.shallow_fbcsp import ShallowFBCSPNet\n",
"from torch import nn\n",
"from braindecode.torch_ext.util import set_random_seeds\n",
"\n",
"# Set if you want to use GPU\n",
"# You can also use torch.cuda.is_available() to determine if cuda is available on your machine.\n",
"cuda = False\n",
"set_random_seeds(seed=20170629, cuda=cuda)\n",
"n_classes = 2\n",
"in_chans = train_set.X.shape[1]\n",
"# final_conv_length = auto ensures we only get a single output in the time dimension\n",
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes,\n",
" input_time_length=train_set.X.shape[2],\n",
" final_conv_length='auto').create_network()\n",
"if cuda:\n",
" model.cuda()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from torch import optim\n",
"\n",
"optimizer = optim.Adam(model.parameters())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0\n",
"Train Loss: 1.17760\n",
"Train Accuracy: 51.7%\n",
"Test Loss: 1.26024\n",
"Test Accuracy: 53.3%\n",
"Epoch 1\n",
"Train Loss: 0.70688\n",
"Train Accuracy: 63.3%\n",
"Test Loss: 0.93679\n",
"Test Accuracy: 56.7%\n",
"Epoch 2\n",
"Train Loss: 0.39426\n",
"Train Accuracy: 86.7%\n",
"Test Loss: 0.65984\n",
"Test Accuracy: 66.7%\n",
"Epoch 3\n",
"Train Loss: 0.33593\n",
"Train Accuracy: 90.0%\n",
"Test Loss: 0.65626\n",
"Test Accuracy: 63.3%\n",
"Epoch 4\n",
"Train Loss: 0.27905\n",
"Train Accuracy: 96.7%\n",
"Test Loss: 0.61044\n",
"Test Accuracy: 56.7%\n",
"Epoch 5\n",
"Train Loss: 0.27319\n",
"Train Accuracy: 96.7%\n",
"Test Loss: 0.59499\n",
"Test Accuracy: 66.7%\n",
"Epoch 6\n",
"Train Loss: 0.26624\n",
"Train Accuracy: 88.3%\n",
"Test Loss: 0.61360\n",
"Test Accuracy: 70.0%\n",
"Epoch 7\n",
"Train Loss: 0.23338\n",
"Train Accuracy: 91.7%\n",
"Test Loss: 0.65626\n",
"Test Accuracy: 73.3%\n",
"Epoch 8\n",
"Train Loss: 0.21903\n",
"Train Accuracy: 90.0%\n",
"Test Loss: 0.68762\n",
"Test Accuracy: 63.3%\n",
"Epoch 9\n",
"Train Loss: 0.18902\n",
"Train Accuracy: 96.7%\n",
"Test Loss: 0.67174\n",
"Test Accuracy: 56.7%\n",
"Epoch 10\n",
"Train Loss: 0.17774\n",
"Train Accuracy: 96.7%\n",
"Test Loss: 0.68129\n",
"Test Accuracy: 56.7%\n",
"Epoch 11\n",
"Train Loss: 0.16396\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.69190\n",
"Test Accuracy: 56.7%\n",
"Epoch 12\n",
"Train Loss: 0.15179\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.69641\n",
"Test Accuracy: 56.7%\n",
"Epoch 13\n",
"Train Loss: 0.15035\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.70514\n",
"Test Accuracy: 60.0%\n",
"Epoch 14\n",
"Train Loss: 0.14255\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.74239\n",
"Test Accuracy: 53.3%\n",
"Epoch 15\n",
"Train Loss: 0.13597\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.77918\n",
"Test Accuracy: 53.3%\n",
"Epoch 16\n",
"Train Loss: 0.13680\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.84160\n",
"Test Accuracy: 56.7%\n",
"Epoch 17\n",
"Train Loss: 0.12191\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.87803\n",
"Test Accuracy: 56.7%\n",
"Epoch 18\n",
"Train Loss: 0.10604\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.90946\n",
"Test Accuracy: 56.7%\n",
"Epoch 19\n",
"Train Loss: 0.10445\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.93717\n",
"Test Accuracy: 56.7%\n",
"Epoch 20\n",
"Train Loss: 0.10497\n",
"Train Accuracy: 98.3%\n",
"Test Loss: 0.95503\n",
"Test Accuracy: 60.0%\n",
"Epoch 21\n",
"Train Loss: 0.10946\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.99562\n",
"Test Accuracy: 60.0%\n",
"Epoch 22\n",
"Train Loss: 0.05721\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.84823\n",
"Test Accuracy: 60.0%\n",
"Epoch 23\n",
"Train Loss: 0.04464\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.78784\n",
"Test Accuracy: 56.7%\n",
"Epoch 24\n",
"Train Loss: 0.04180\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.78525\n",
"Test Accuracy: 56.7%\n",
"Epoch 25\n",
"Train Loss: 0.03730\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.75642\n",
"Test Accuracy: 63.3%\n",
"Epoch 26\n",
"Train Loss: 0.03549\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.71707\n",
"Test Accuracy: 66.7%\n",
"Epoch 27\n",
"Train Loss: 0.03416\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.69796\n",
"Test Accuracy: 66.7%\n",
"Epoch 28\n",
"Train Loss: 0.03205\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.69325\n",
"Test Accuracy: 66.7%\n",
"Epoch 29\n",
"Train Loss: 0.02805\n",
"Train Accuracy: 100.0%\n",
"Test Loss: 0.69243\n",
"Test Accuracy: 63.3%\n"
]
}
],
"source": [
"from braindecode.torch_ext.util import np_to_var, var_to_np\n",
"from braindecode.datautil.iterators import get_balanced_batches\n",
"import torch.nn.functional as F\n",
"from numpy.random import RandomState\n",
"rng = RandomState()\n",
"for i_epoch in range(30):\n",
" i_trials_in_batch = get_balanced_batches(len(train_set.X), rng, shuffle=True,\n",
" batch_size=30)\n",
" # Set model to training mode\n",
" model.train()\n",
" for i_trials in i_trials_in_batch:\n",
" # Have to add empty fourth dimension to X\n",
" batch_X = train_set.X[i_trials][:,:,:,None]\n",
" batch_y = train_set.y[i_trials]\n",
" net_in = np_to_var(batch_X)\n",
" if cuda:\n",
" net_in = net_in.cuda()\n",
" net_target = np_to_var(batch_y)\n",
" if cuda:\n",
" net_target = net_target.cuda()\n",
" # Remove gradients of last backward pass from all parameters\n",
" optimizer.zero_grad()\n",
" # Compute outputs of the network\n",
" outputs = model(net_in)\n",
" # Compute the loss\n",
" loss = F.nll_loss(outputs, net_target)\n",
" # Do the backpropagation\n",
" loss.backward()\n",
" # Update parameters with the optimizer\n",
" optimizer.step()\n",
"\n",
" # Print some statistics each epoch\n",
" model.eval()\n",
" print(\"Epoch {:d}\".format(i_epoch))\n",
" for setname, dataset in (('Train', train_set), ('Test', test_set)):\n",
" # Here, we will use the entire dataset at once, which is still possible\n",
" # for such smaller datasets. Otherwise we would have to use batches.\n",
" net_in = np_to_var(dataset.X[:,:,:,None])\n",
" if cuda:\n",
" net_in = net_in.cuda()\n",
" net_target = np_to_var(dataset.y)\n",
" if cuda:\n",
" net_target = net_target.cuda()\n",
" outputs = model(net_in)\n",
" loss = F.nll_loss(outputs, net_target)\n",
" print(\"{:6s} Loss: {:.5f}\".format(\n",
" setname, float(var_to_np(loss))))\n",
" predicted_labels = np.argmax(var_to_np(outputs), axis=1)\n",
" accuracy = np.mean(dataset.y == predicted_labels)\n",
" print(\"{:6s} Accuracy: {:.1f}%\".format(\n",
" setname, accuracy * 100))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import mne\n",
"import numpy as np\n",
"from mne.io import concatenate_raws\n",
"from braindecode.datautil.signal_target import SignalAndTarget\n",
"\n",
"physionet_paths = [ mne.datasets.eegbci.load_data(sub_id,[4,8,12,]) for sub_id in range(1,51)]\n",
"physionet_paths = np.concatenate(physionet_paths)\n",
"parts = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto')\n",
" for path in physionet_paths]\n",
"\n",
"raw = concatenate_raws(parts)\n",
"\n",
"picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n",
" exclude='bads')\n",
"\n",
"events = mne.find_events(raw, shortest_event=0, stim_channel='STI 014')\n",
"\n",
"# Read epochs (train will be done only between 1 and 2s)\n",
"# Testing will be done with a running classifier\n",
"epoched = mne.Epochs(raw, events, dict(hands=2, feet=3), tmin=1, tmax=4.1, proj=False, picks=picks,\n",
" baseline=None, preload=True)\n",
"\n",
"physionet_paths_test = [mne.datasets.eegbci.load_data(sub_id,[4,8,12,]) for sub_id in range(51,56)]\n",
"physionet_paths_test = np.concatenate(physionet_paths_test)\n",
"parts_test = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto')\n",
" for path in physionet_paths_test]\n",
"raw_test = concatenate_raws(parts_test)\n",
"\n",
"picks_test = mne.pick_types(raw_test.info, meg=False, eeg=True, stim=False, eog=False,\n",
" exclude='bads')\n",
"\n",
"events_test = mne.find_events(raw_test, shortest_event=0, stim_channel='STI 014')\n",
"\n",
"# Read epochs (train will be done only between 1 and 2s)\n",
"# Testing will be done with a running classifier\n",
"epoched_test = mne.Epochs(raw_test, events_test, dict(hands=2, feet=3), tmin=1, tmax=4.1, proj=False, picks=picks_test,\n",
" baseline=None, preload=True)\n",
"\n",
"train_X = (epoched.get_data() * 1e6).astype(np.float32)\n",
"train_y = (epoched.events[:,2] - 2).astype(np.int64) #2,3 -> 0,1\n",
"test_X = (epoched_test.get_data() * 1e6).astype(np.float32)\n",
"test_y = (epoched_test.events[:,2] - 2).astype(np.int64) #2,3 -> 0,1\n",
"train_set = SignalAndTarget(train_X, y=train_y)\n",
"test_set = SignalAndTarget(test_X, y=test_y)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}