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ERP.py
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ERP.py
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"""
Sample script using EEGNet to classify Event-Related Potential (ERP) EEG data
from a four-class classification task, using the sample dataset provided in
the MNE [1, 2] package:
https://martinos.org/mne/stable/manual/sample_dataset.html#ch-sample-data
The four classes used from this dataset are:
LA: Left-ear auditory stimulation
RA: Right-ear auditory stimulation
LV: Left visual field stimulation
RV: Right visual field stimulation
The code to process, filter and epoch the data are originally from Alexandre
Barachant's PyRiemann [3] package, released under the BSD 3-clause. A copy of
the BSD 3-clause license has been provided together with this software to
comply with software licensing requirements.
When you first run this script, MNE will download the dataset and prompt you
to confirm the download location (defaults to ~/mne_data). Follow the prompts
to continue. The dataset size is approx. 1.5GB download.
For comparative purposes you can also compare EEGNet performance to using
Riemannian geometric approaches with xDAWN spatial filtering [4-8] using
PyRiemann (code provided below).
[1] A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck,
L. Parkkonen, M. Hämäläinen, MNE software for processing MEG and EEG data,
NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-8119.
[2] A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck,
R. Goj, M. Jas, T. Brooks, L. Parkkonen, M. Hämäläinen, MEG and EEG data
analysis with MNE-Python, Frontiers in Neuroscience, Volume 7, 2013.
[3] https://github.com/alexandrebarachant/pyRiemann.
[4] A. Barachant, M. Congedo ,"A Plug&Play P300 BCI Using Information Geometry"
arXiv:1409.0107. link
[5] M. Congedo, A. Barachant, A. Andreev ,"A New generation of Brain-Computer
Interface Based on Riemannian Geometry", arXiv: 1310.8115.
[6] A. Barachant and S. Bonnet, "Channel selection procedure using riemannian
distance for BCI applications," in 2011 5th International IEEE/EMBS
Conference on Neural Engineering (NER), 2011, 348-351.
[7] A. Barachant, S. Bonnet, M. Congedo and C. Jutten, “Multiclass
Brain-Computer Interface Classification by Riemannian Geometry,” in IEEE
Transactions on Biomedical Engineering, vol. 59, no. 4, p. 920-928, 2012.
[8] A. Barachant, S. Bonnet, M. Congedo and C. Jutten, “Classification of
covariance matrices using a Riemannian-based kernel for BCI applications“,
in NeuroComputing, vol. 112, p. 172-178, 2013.
Portions of this project are works of the United States Government and are not
subject to domestic copyright protection under 17 USC Sec. 105. Those
portions are released world-wide under the terms of the Creative Commons Zero
1.0 (CC0) license.
Other portions of this project are subject to domestic copyright protection
under 17 USC Sec. 105. Those portions are licensed under the Apache 2.0
license. The complete text of the license governing this material is in
the file labeled LICENSE.TXT that is a part of this project's official
distribution.
"""
import numpy as np
# mne imports
import mne
from mne import io
from mne.datasets import sample
# EEGNet-specific imports
from EEGModels import EEGNet
from tensorflow.keras import utils as np_utils
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras import backend as K
# PyRiemann imports
from pyriemann.estimation import XdawnCovariances
from pyriemann.tangentspace import TangentSpace
from pyriemann.utils.viz import plot_confusion_matrix
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
# tools for plotting confusion matrices
from matplotlib import pyplot as plt
# while the default tensorflow ordering is 'channels_last' we set it here
# to be explicit in case if the user has changed the default ordering
K.set_image_data_format('channels_last')
##################### Process, filter and epoch the data ######################
data_path = sample.data_path()
# Set parameters and read data
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
tmin, tmax = -0., 1
event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)
# Setup for reading the raw data
raw = io.Raw(raw_fname, preload=True, verbose=False)
raw.filter(2, None, method='iir') # replace baselining with high-pass
events = mne.read_events(event_fname)
raw.info['bads'] = ['MEG 2443'] # set bad channels
picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False,
picks=picks, baseline=None, preload=True, verbose=False)
labels = epochs.events[:, -1]
# extract raw data. scale by 1000 due to scaling sensitivity in deep learning
X = epochs.get_data()*1000 # format is in (trials, channels, samples)
y = labels
kernels, chans, samples = 1, 60, 151
# take 50/25/25 percent of the data to train/validate/test
X_train = X[0:144,]
Y_train = y[0:144]
X_validate = X[144:216,]
Y_validate = y[144:216]
X_test = X[216:,]
Y_test = y[216:]
############################# EEGNet portion ##################################
# convert labels to one-hot encodings.
Y_train = np_utils.to_categorical(Y_train-1)
Y_validate = np_utils.to_categorical(Y_validate-1)
Y_test = np_utils.to_categorical(Y_test-1)
# convert data to NHWC (trials, channels, samples, kernels) format. Data
# contains 60 channels and 151 time-points. Set the number of kernels to 1.
X_train = X_train.reshape(X_train.shape[0], chans, samples, kernels)
X_validate = X_validate.reshape(X_validate.shape[0], chans, samples, kernels)
X_test = X_test.reshape(X_test.shape[0], chans, samples, kernels)
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# configure the EEGNet-8,2,16 model with kernel length of 32 samples (other
# model configurations may do better, but this is a good starting point)
model = EEGNet(nb_classes = 4, Chans = chans, Samples = samples,
dropoutRate = 0.5, kernLength = 32, F1 = 8, D = 2, F2 = 16,
dropoutType = 'Dropout')
# compile the model and set the optimizers
model.compile(loss='categorical_crossentropy', optimizer='adam',
metrics = ['accuracy'])
# count number of parameters in the model
numParams = model.count_params()
# set a valid path for your system to record model checkpoints
checkpointer = ModelCheckpoint(filepath='/tmp/checkpoint.h5', verbose=1,
save_best_only=True)
###############################################################################
# if the classification task was imbalanced (significantly more trials in one
# class versus the others) you can assign a weight to each class during
# optimization to balance it out. This data is approximately balanced so we
# don't need to do this, but is shown here for illustration/completeness.
###############################################################################
# the syntax is {class_1:weight_1, class_2:weight_2,...}. Here just setting
# the weights all to be 1
class_weights = {0:1, 1:1, 2:1, 3:1}
################################################################################
# fit the model. Due to very small sample sizes this can get
# pretty noisy run-to-run, but most runs should be comparable to xDAWN +
# Riemannian geometry classification (below)
################################################################################
fittedModel = model.fit(X_train, Y_train, batch_size = 16, epochs = 300,
verbose = 2, validation_data=(X_validate, Y_validate),
callbacks=[checkpointer], class_weight = class_weights)
# load optimal weights
model.load_weights('/tmp/checkpoint.h5')
###############################################################################
# can alternatively used the weights provided in the repo. If so it should get
# you 93% accuracy. Change the WEIGHTS_PATH variable to wherever it is on your
# system.
###############################################################################
# WEIGHTS_PATH = /path/to/EEGNet-8-2-weights.h5
# model.load_weights(WEIGHTS_PATH)
###############################################################################
# make prediction on test set.
###############################################################################
probs = model.predict(X_test)
preds = probs.argmax(axis = -1)
acc = np.mean(preds == Y_test.argmax(axis=-1))
print("Classification accuracy: %f " % (acc))
############################# PyRiemann Portion ##############################
# code is taken from PyRiemann's ERP sample script, which is decoding in
# the tangent space with a logistic regression
n_components = 2 # pick some components
# set up sklearn pipeline
clf = make_pipeline(XdawnCovariances(n_components),
TangentSpace(metric='riemann'),
LogisticRegression())
preds_rg = np.zeros(len(Y_test))
# reshape back to (trials, channels, samples)
X_train = X_train.reshape(X_train.shape[0], chans, samples)
X_test = X_test.reshape(X_test.shape[0], chans, samples)
# train a classifier with xDAWN spatial filtering + Riemannian Geometry (RG)
# labels need to be back in single-column format
clf.fit(X_train, Y_train.argmax(axis = -1))
preds_rg = clf.predict(X_test)
# Printing the results
acc2 = np.mean(preds_rg == Y_test.argmax(axis = -1))
print("Classification accuracy: %f " % (acc2))
# plot the confusion matrices for both classifiers
names = ['audio left', 'audio right', 'vis left', 'vis right']
plt.figure(0)
plot_confusion_matrix(preds, Y_test.argmax(axis = -1), names, title = 'EEGNet-8,2')
plt.figure(1)
plot_confusion_matrix(preds_rg, Y_test.argmax(axis = -1), names, title = 'xDAWN + RG')