moabb/examples/plot_braindecode.py at develop · goodcpp/moabb · GitHub

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"""

===============================================================

Cross-session motor imagery with deep learning EEGNet v4 model

===============================================================

This example shows how to use BrainDecode in combination with MOABB evaluation.

In this example, we use the architecture EEGNetv4.

"""

# Authors: Igor Carrara <igor.carrara@inria.fr>

# Bruno Aristimunha <b.aristimunha@gmail.com>

# License: BSD (3-clause)

import matplotlib.pyplot as plt

import mne

import seaborn as sns

import torch

from braindecode import EEGClassifier

from braindecode.models import EEGNetv4

from sklearn.pipeline import make_pipeline

from skorch.callbacks import EarlyStopping, EpochScoring

from skorch.dataset import ValidSplit

from moabb.datasets import BNCI2014_001

from moabb.evaluations import CrossSessionEvaluation

from moabb.paradigms import MotorImagery

from moabb.utils import setup_seed

mne.set_log_level(False)

# Print Information PyTorch

print(f"Torch Version: {torch.__version__}")

# Set up GPU if it is there

cuda = torch.cuda.is_available()

device = "cuda" if cuda else "cpu"

print("GPU is", "AVAILABLE" if cuda else "NOT AVAILABLE")

###############################################################################

# In this example, we will use only the dataset ``BNCI2014_001``.

# Running the benchmark

# ---------------------

# This example uses the CrossSession evaluation procedure. We focus on the dataset BNCI2014_001 and only on 1 subject

# to reduce computational time.

# To keep the computational time low, the epoch is reduced. In a real situation, we suggest using the following:

# EPOCH = 1000

# PATIENCE = 300

# This code is implemented to run on the CPU. If you're using a GPU, do not use multithreading

# (i.e. set n_jobs=1)

# Set random seed to be able to reproduce results

seed = 42

setup_seed(seed)

# Ensure that all operations are deterministic on GPU (if used) for reproducibility

torch.backends.cudnn.deterministic = True

torch.backends.cudnn.benchmark = False

# Hyperparameter

LEARNING_RATE = 0.0625 * 0.01 # parameter taken from Braindecode

WEIGHT_DECAY = 0 # parameter taken from Braindecode

BATCH_SIZE = 64 # parameter taken from BrainDecode

EPOCH = 10

PATIENCE = 3

fmin = 4

fmax = 100

tmin = 0

tmax = None

# Load the dataset

dataset = BNCI2014_001()

events = ["right_hand", "left_hand"]

paradigm = MotorImagery(

events=events, n_classes=len(events), fmin=fmin, fmax=fmax, tmin=tmin, tmax=tmax

)

subjects = [1]

X, _, _ = paradigm.get_data(dataset=dataset, subjects=subjects)

##############################################################################

# Create Pipelines

# ----------------

# In order to create a pipeline, we need to load a model from braindecode.

# the second step is to define a skorch model using EEGClassifier from braindecode

# that allows converting the PyTorch model in a scikit-learn classifier.

# Here, we will use the EEGNet v4 model [1]_ .

# This model has mandatory hyperparameters (the number of channels, the number of classes,

# and the temporal length of the input) but we do not need to specify them because they will

# be set dynamically by EEGClassifier using the input data during the call to the ``.fit()`` method.

# Define a Skorch classifier

clf = EEGClassifier(

module=EEGNetv4,

optimizer=torch.optim.Adam,

optimizer__lr=LEARNING_RATE,

batch_size=BATCH_SIZE,

max_epochs=EPOCH,

train_split=ValidSplit(0.2, random_state=seed),

device=device,

callbacks=[

EarlyStopping(monitor="valid_loss", patience=PATIENCE),

EpochScoring(

scoring="accuracy", on_train=True, name="train_acc", lower_is_better=False

EpochScoring(

scoring="accuracy", on_train=False, name="valid_acc", lower_is_better=False

verbose=1, # Not printing the results for each epoch

)

# Create the pipelines

pipes = {}

pipes["EEGNetV4"] = make_pipeline(clf)

##############################################################################

# Evaluation

# ----------

dataset.subject_list = dataset.subject_list[:2]

evaluation = CrossSessionEvaluation(

paradigm=paradigm,

datasets=dataset,

suffix="braindecode_example",

overwrite=True,

return_epochs=True,

n_jobs=1,

)

results = evaluation.process(pipes)

print(results.head())

##############################################################################

# Plot Results

# ----------------

plt.figure()

sns.barplot(data=results, y="score", x="subject", palette="viridis")

plt.show()

##############################################################################

# References

# ----------

# .. [1] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M.,

# Hung, C. P., & Lance, B. J. (2018). `EEGNet: a compact convolutional neural

# network for EEG-based brain-computer interfaces.

# <https://doi.org/10.1088/1741-2552/aace8c>`_

# Journal of neural engineering, 15(5), 056013.

moabb/examples/plot_braindecode.py at develop · goodcpp/moabb · GitHub

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