Benchmarking on MOABB with Tensorflow deep net architectures

This example shows how to use MOABB to benchmark a set of Deep Learning pipeline (Tensorflow) on all available datasets. For this example, we will use only one dataset to keep the computation time low, but this benchmark is designed to easily scale to many datasets.

# Authors: Igor Carrara <igor.carrara@inria.fr>
#
# License: BSD (3-clause)

import os

import matplotlib.pyplot as plt
import tensorflow as tf
from absl.logging import ERROR, set_verbosity
from tensorflow import keras

from moabb import benchmark, set_log_level
from moabb.analysis.plotting import score_plot
from moabb.datasets import BNCI2014_001
from moabb.utils import setup_seed


set_log_level("info")
# Avoid output Warning
set_verbosity(ERROR)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"

# Print Information Tensorflow
print(f"Tensorflow Version: {tf.__version__}")
print(f"Keras Version: {keras.__version__}")

CPU = len(tf.config.list_physical_devices("CPU")) > 0
print("CPU is", "AVAILABLE" if CPU else "NOT AVAILABLE")

GPU = len(tf.config.list_physical_devices("GPU")) > 0
print("GPU is", "AVAILABLE" if GPU else "NOT AVAILABLE")

Tensorflow Version: 2.12.1
Keras Version: 2.12.0
CPU is AVAILABLE
GPU is NOT AVAILABLE

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

Running the benchmark

The benchmark is run using the benchmark function. You need to specify the folder containing the pipelines to use, the kind of evaluation and the paradigm to use. By default, the benchmark will use all available datasets for all paradigms listed in the pipelines. You could restrict to specific evaluation and paradigm using the evaluations and paradigms arguments.

To save computation time, the results are cached. If you want to re-run the benchmark, you can set the overwrite argument to True.

It is possible to indicate the folder to cache the results and the one to save the analysis & figures. By default, the results are saved in the results folder, and the analysis & figures are saved in the benchmark folder.

This code is implemented to run on CPU. If you’re using a GPU, do not use multithreading (i.e. set n_jobs=1)

# Set up reproducibility of Tensorflow
setup_seed(42)

# Restrict this example only on the first two subject of BNCI2014_001
dataset = BNCI2014_001()
dataset.subject_list = dataset.subject_list[:2]
datasets = [dataset]

results = benchmark(
    pipelines="./pipelines_DL",
    evaluations=["WithinSession"],
    paradigms=["LeftRightImagery"],
    include_datasets=datasets,
    results="./results/",
    overwrite=False,
    plot=False,
    output="./benchmark/",
    n_jobs=-1,
)

BNCI2014-001-WithinSession:   0%|          | 0/2 [00:00<?, ?it/s]/home/runner/work/moabb/moabb/moabb/datasets/download.py:55: RuntimeWarning: Setting non-standard config type: "MNE_DATASETS_BNCI_PATH"
  set_config(key, get_config("MNE_DATA"))
/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'lampx.tugraz.at'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
  warnings.warn(


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/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'lampx.tugraz.at'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
  warnings.warn(


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/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
  X = mne.concatenate_epochs(X)

BNCI2014-001-WithinSession:  50%|#####     | 1/2 [04:31<04:31, 271.33s/it]/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'lampx.tugraz.at'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
  warnings.warn(


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/home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'lampx.tugraz.at'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
  warnings.warn(


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/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/datasets/preprocessing.py:273: UserWarning: warnEpochs <Epochs |  24 events (all good), 2 – 6 s, baseline off, ~4.1 MB, data loaded,
 'left_hand': 12
 'right_hand': 12>
  warn(f"warnEpochs {epochs}")
/home/runner/work/moabb/moabb/moabb/paradigms/base.py:355: RuntimeWarning: Concatenation of Annotations within Epochs is not supported yet. All annotations will be dropped.
  X = mne.concatenate_epochs(X)

BNCI2014-001-WithinSession: 100%|##########| 2/2 [09:07<00:00, 273.91s/it]
BNCI2014-001-WithinSession: 100%|##########| 2/2 [09:07<00:00, 273.52s/it]
        dataset     evaluation              pipeline  avg score
0  BNCI2014-001  WithinSession     Keras_DeepConvNet   0.522551
1  BNCI2014-001  WithinSession        Keras_EEGTCNet   0.462585
2  BNCI2014-001  WithinSession          Keras_EEGNeX   0.534371
3  BNCI2014-001  WithinSession  Keras_ShallowConvNet   0.612891
4  BNCI2014-001  WithinSession        Keras_EEGITNet   0.564524
5  BNCI2014-001  WithinSession      Keras_EEGNet_8_2   0.488010

The deep learning architectures implemented in MOABB are: - Shallow Convolutional Network [1] - Deep Convolutional Network [1] - EEGNet [2] - EEGTCNet [3] - EEGNex [4] - EEGITNet [5]

Benchmark prints a summary of the results. Detailed results are saved in a pandas dataframe, and can be used to generate figures. The analysis & figures are saved in the benchmark folder.

score_plot(results)
plt.show()

References

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Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., … & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human brain mapping, 38(11), 5391-5420.

[2]

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

[3]

Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., & Benini, L. (2020, October). EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain-machine interfaces. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2958-2965). IEEE.

[4]

Chen, X., Teng, X., Chen, H., Pan, Y., & Geyer, P. (2022). Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX. arXiv preprint arXiv:2207.12369.

[5]

Salami, A., Andreu-Perez, J., & Gillmeister, H. (2022). EEG-ITNet: An explainable inception temporal convolutional network for motor imagery classification. IEEE Access, 10, 36672-36685.

Estimated memory usage: 855 MB