Note

Go to the end to download the full example code.

Tutorial 0: Getting Started#

This tutorial takes you through a basic working example of how to use this codebase, including all the different components, up to the results generation. If you’d like to know about the statistics and plotting, see the next tutorial.

# Authors: Vinay Jayaram <vinayjayaram13@gmail.com>
#
# License: BSD (3-clause)

Introduction#

To use the codebase you need an evaluation and a paradigm, some algorithms, and a list of datasets to run it all on. You can find those in the following submodules; detailed tutorials are given for each of them.

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC

If you would like to specify the logging level when it is running, you can use the standard python logging commands through the top-level moabb module

import moabb
from moabb.datasets import BNCI2014_001, utils
from moabb.evaluations import CrossSessionEvaluation
from moabb.paradigms import LeftRightImagery
from moabb.pipelines.features import LogVariance

In order to create pipelines within a script, you will likely need at least the make_pipeline function. They can also be specified via a .yml file. Here we will make a couple pipelines just for convenience

moabb.set_log_level("info")

Create pipelines#

We create two pipelines: channel-wise log variance followed by LDA, and channel-wise log variance followed by a cross-validated SVM (note that a cross-validation via scikit-learn cannot be described in a .yml file). For later in the process, the pipelines need to be in a dictionary where the key is the name of the pipeline and the value is the Pipeline object

pipelines = {}
pipelines["AM+LDA"] = make_pipeline(LogVariance(), LDA())
parameters = {"C": np.logspace(-2, 2, 10)}
clf = GridSearchCV(SVC(kernel="linear"), parameters)
pipe = make_pipeline(LogVariance(), clf)

pipelines["AM+SVM"] = pipe

Datasets#

Datasets can be specified in many ways: Each paradigm has a property ‘datasets’ which returns the datasets that are appropriate for that paradigm

print(LeftRightImagery().datasets)

/home/runner/work/moabb/moabb/moabb/datasets/fake.py:92: RuntimeWarning: Setting non-standard config type: "MNE_DATASETS_FAKEDATASET-IMAGERY-10-2--60-60--120-120--FAKE1-FAKE2-FAKE3--C3-CZ-C4_PATH"
  set_config(key, temp_dir)
/home/runner/work/moabb/moabb/moabb/datasets/fake.py:92: RuntimeWarning: Setting non-standard config type: "MNE_DATASETS_FAKEVIRTUALREALITYDATASET-P300-21-1--60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60-60--120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120-120--TARGET-NONTARGET--C3-CZ-C4_PATH"
  set_config(key, temp_dir)
/home/runner/work/moabb/moabb/moabb/datasets/romani_bf2025_erp.py:218: RuntimeWarning: Setting non-standard config type: "RomaniBF2025ERP"
  path_root = fetch_dataset(
[<moabb.datasets.bnci.BNCI2014_001 object at 0x7f4ba5a5d810>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f4ba5f47b50>, <moabb.datasets.beetl.Beetl2021_A object at 0x7f4ba5f44310>, <moabb.datasets.beetl.Beetl2021_B object at 0x7f4ba5f44400>, <moabb.datasets.gigadb.Cho2017 object at 0x7f4ba5f45360>, <moabb.datasets.dreyer2023.Dreyer2023 object at 0x7f4ba5f47a30>, <moabb.datasets.dreyer2023.Dreyer2023A object at 0x7f4ba5a5f730>, <moabb.datasets.dreyer2023.Dreyer2023B object at 0x7f4ba5f45330>, <moabb.datasets.dreyer2023.Dreyer2023C object at 0x7f4ba5f47640>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f4ba5f44340>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f4ba6004730>, <moabb.datasets.liu2024.Liu2024 object at 0x7f4ba5ecd870>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f4ba60065f0>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f4ba586fb20>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f4ba586fe20>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7f4ba586d240>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f4ba586c790>, <moabb.datasets.Zhou2016.Zhou2016 object at 0x7f4ba586faf0>]

Or you can run a search through the available datasets:

print(utils.dataset_search(paradigm="imagery", min_subjects=6))

[<moabb.datasets.alex_mi.AlexMI object at 0x7f4ba5a5fb20>, <moabb.datasets.bnci.BNCI2014_001 object at 0x7f4ba5f45330>, <moabb.datasets.bnci.BNCI2014_002 object at 0x7f4ba5f47640>, <moabb.datasets.bnci.BNCI2014_004 object at 0x7f4ba5f47b50>, <moabb.datasets.bnci.BNCI2015_001 object at 0x7f4ba5f44400>, <moabb.datasets.bnci.BNCI2015_004 object at 0x7f4ba586fbb0>, <moabb.datasets.gigadb.Cho2017 object at 0x7f4ba5f47a30>, <moabb.datasets.dreyer2023.Dreyer2023 object at 0x7f4ba586ea70>, <moabb.datasets.dreyer2023.Dreyer2023A object at 0x7f4ba586faf0>, <moabb.datasets.dreyer2023.Dreyer2023B object at 0x7f4ba586de70>, <moabb.datasets.dreyer2023.Dreyer2023C object at 0x7f4ba586dc60>, <moabb.datasets.fake.FakeDataset object at 0x7f4ba586d8a0>, <moabb.datasets.mpi_mi.GrosseWentrup2009 object at 0x7f4ba586d240>, <moabb.datasets.Lee2019.Lee2019_MI object at 0x7f4ba586cbb0>, <moabb.datasets.liu2024.Liu2024 object at 0x7f4ba586fe20>, <moabb.datasets.upper_limb.Ofner2017 object at 0x7f4ba586e410>, <moabb.datasets.physionet_mi.PhysionetMI object at 0x7f4ba586fb20>, <moabb.datasets.schirrmeister2017.Schirrmeister2017 object at 0x7f4ba5a5e7d0>, <moabb.datasets.bbci_eeg_fnirs.Shin2017A object at 0x7f4ba5e68790>, <moabb.datasets.bbci_eeg_fnirs.Shin2017B object at 0x7f4ba5e68d30>, <moabb.datasets.stieger2021.Stieger2021 object at 0x7f4ba5e6a8c0>, <moabb.datasets.Weibo2014.Weibo2014 object at 0x7f4ba5e6aec0>]

Or you can simply make your own list (which we do here due to computational constraints)

dataset = BNCI2014_001()
dataset.subject_list = dataset.subject_list[:2]
datasets = [dataset]

Paradigm#

Paradigms define the events, epoch time, bandpass, and other preprocessing parameters. They have defaults that you can read in the documentation, or you can simply set them as we do here. A single paradigm defines a method for going from continuous data to trial data of a fixed size. To learn more look at the tutorial Exploring Paradigms

fmin = 8
fmax = 35
paradigm = LeftRightImagery(fmin=fmin, fmax=fmax)

Evaluation#

An evaluation defines how the training and test sets are chosen. This could be cross-validated within a single recording, or across days, or sessions, or subjects. This also is the correct place to specify multiple threads.

evaluation = CrossSessionEvaluation(
    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=False
)
results = evaluation.process(pipelines)

/home/runner/work/moabb/moabb/moabb/analysis/results.py:95: RuntimeWarning: Setting non-standard config type: "MOABB_RESULTS"
  set_config("MOABB_RESULTS", osp.join(osp.expanduser("~"), "mne_data"))

BNCI2014-001-CrossSession:   0%|          | 0/2 [00:00<?, ?it/s]2025-12-14 05:51:28,712 INFO MainThread moabb.datasets.download MNE_DATA is not already configured. It will be set to default location in the home directory - /home/runner/mne_data
All datasets will be downloaded to this location, if anything is already downloaded, please move manually to this location
/home/runner/work/moabb/moabb/moabb/datasets/download.py:60: RuntimeWarning: Setting non-standard config type: "MNE_DATASETS_BNCI_PATH"
  set_config(key, get_config("MNE_DATA"))

BNCI2014-001-CrossSession:  50%|█████     | 1/2 [00:03<00:03,  3.41s/it]
BNCI2014-001-CrossSession: 100%|██████████| 2/2 [00:06<00:00,  3.05s/it]
BNCI2014-001-CrossSession: 100%|██████████| 2/2 [00:06<00:00,  3.10s/it]
2025-12-14 05:51:34,663 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 1 | 0train: Score 0.786
2025-12-14 05:51:34,784 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 1 | 1test: Score 0.802
2025-12-14 05:51:34,903 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 1 | 0train: Score 0.797
2025-12-14 05:51:35,038 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 1 | 1test: Score 0.774
2025-12-14 05:51:35,171 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 2 | 0train: Score 0.577
2025-12-14 05:51:35,289 INFO MainThread moabb.evaluations.base AM+LDA | BNCI2014-001 | 2 | 1test: Score 0.499
2025-12-14 05:51:35,410 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 2 | 0train: Score 0.551
2025-12-14 05:51:35,541 INFO MainThread moabb.evaluations.base AM+SVM | BNCI2014-001 | 2 | 1test: Score 0.471

Results are returned as a pandas DataFrame, and from here you can do as you want with them

print(results.head())

      score      time  samples  ... n_sessions       dataset  pipeline
0  0.786458  0.030805    144.0  ...          2  BNCI2014-001    AM+LDA
1  0.801698  0.029237    144.0  ...          2  BNCI2014-001    AM+LDA
2  0.576582  0.023756    144.0  ...          2  BNCI2014-001    AM+LDA
3  0.498650  0.021339    144.0  ...          2  BNCI2014-001    AM+LDA
4  0.797068  0.150556    144.0  ...          2  BNCI2014-001    AM+SVM

[5 rows x 9 columns]

Total running time of the script: (0 minutes 13.216 seconds)

Estimated memory usage: 798 MB

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