What’s new

• “Enhancements” for new features

• “Bugs” for bug fixes

• “API changes” for backward-incompatible changes

Version 1.5 (Source - GitHub)

🚀 Enhancements

🧐 API and behavior changes

🛠 Requirements

🪲 Bug fixes

⚕️ Code health

• Fixing warnings from the latest scikit-learn version within the Preprocessing logic (#850 by Bruno Aristimunha)

Version 1.4.2 (Stable - PyPi)

• Adding dataset moabb.datasets.RomaniBF2025ERP converted to BIDS (#825 by Romani Michele)

• Improve compute_pvals_perm function (#818 by Quentin Barthelemy)

Bugs - Fixes the management of include/exclude datasets in moabb.benchmark(), adds additional verifications (#834 by Anton Andreev) - Fixing pagination issue with figshare (#839 by Bruno Aristimunha) - Fixes moabb.datasets.preprocessing.SetRawAnnotations in case no STIM channel is present (#838 by Pierre Guetschel and Simon Kojima)

Version - 1.4

• Update to pyRiemann 0.9 and numpy 2.0 for improved compatibility (#789 by Gregoire Cattan and Bruno Aristimunha)

• Adding moabb.datasets.Kojima2024A (#807 by Simon Kojima)

• Adding moabb.datasets.Kojima2024B (#806 by Simon Kojima)

• Add new dataset moabb.datasets.BNCI2003_IVa dataset (#811 by Griffin Keeler)

• Added the ability to feed pipelines using a list of dictionaries in moabb.benchmark() (#826 by Anton Andreev)

• Fixing label swapped issue with moabb.datasets.Kalunga2016 dataset (#814 by Griffin Keeler)

• Fix the moabb.datasets.Dreyer2023 (#828 by Simon Kojima)

API changes

---

Version - 1.3

• Adding a tutorial for moabb.evaluations.splitters.WithinSessionSplitter (#776 by Thomas Kooiman, Paul Verhoeven, Jorge Sanmartin Martinez, and Radovan Vodila )

• Adding new motor imagery dataset, Dreyer2023 (#404 by Sara Sedlar, Sylvain Chevallier and Bruno Aristimunha)

• Reordering the examples in the documentation (#706 by Bruno Aristimunha)

• Creating the meta information for the BIDS converted datasets (#688 by Bruno Aristimunha)

• Adding moabb.datasets.Beetl2021_A and moabb.datasets.Beetl2021_B (#675 by Samuel Boehm)

• Adding moabb.evaluations.splitters.CrossSubjectSplitter (#722 by Bruna Lopes and Bruno Aristimunha)

• Adding moabb.evaluations.splitters.CrossSessionSplitter (#720 by Bruna Lopes and Bruno Aristimunha)

• Adding moabb.datasets.base.BaseBIDSDataset and moabb.datasets.base.LocalBIDSDataset (#724 by Pierre Guetschel)

• Adding moabb.analysis.plotting.dataset_bubble_plot() plus the corresponding tutorial (#753 by Pierre Guetschel)

• Adding moabb.datasets.utils.plot_all_datasets() and update the tutorial (#758 by Pierre Guetschel)

• Improve the dataset model cards in each API page (#765 by Pierre Guetschel)

• Refactor moabb.evaluation.CrossSessionEvaluation, moabb.evaluation.CrossSubjectEvaluation and moabb.evaluation.WithinSessionEvaluation to use the new splitter classes (#769 by Bruno Aristimunha)

• Adding tutorial on using mne-features (#762 by Alexander de Ranitz, Luuk Neervens, Charlynn van Osch and Bruno Aristimunha)

• Creating tutorial to expose the pre-processing steps (#771 by Bruno Aristimunha)

• Add function to auto-generate tables for the paper results documentation page (#785 by Lucas Heck)

• Improving the Filterbank tutorial and implementing the mutual information selection to reproduce the FilterbankCSP (#787 by Bruno Aristimunha)

• A tutorial on how to create and use a MOABB dataset from X y (non continuous, epoched) data (#800 by Anton Andreev)

• Improving the parallel writing of results (#803 by Bruno Aristimunha)

• Fix regression in evaluations ignoring process_pipeline flag (#774 by Bruno Aristimunha)

• Fix caching issue with incomplete results (#715 by Sylvain Chevallier)

• Fix learning curve example (#717 by Pierre Guetschel)

• Pick all data channels in filter preprocessing step (#729 by Pierre Guetschel)

• Fix CI for permutation testing (#757 by Quentin Barthelemy)

• Fix download issue with Schirrmeister2017 dataset (#751 by Zheyu Yao)

• Fix code carbon example code (#777 by Amar Enkhbat)

• Including the fix_bad_channels for the moabb.datasets.Stieger2021 (#783 by Bruno Aristimunha)

• Fix the moabb.datasets.Wang2016 (#781 by Ulysse Durand)

• Fix warnings raised when building the documentation (#784 by Lucas Heck)

• Remove an unnecessary line in the README.md (#791 by `Lionel Kusch`_)

• Update the dead link about the tutorial of GitHub in CONTRIBUTING.md (#792 by `Lionel Kusch`_)

• Fix: number of trial per class for PHMD_ML dataset (#797 by Gregoire Cattan)

• Converting the moabb.datasets.Zhou2016 to BIDS (#802 by Bruno Aristimunha)

• Removing the deep learning module from inside moabb in favour of braindecode integration (#692 by Bruno Aristimunha )

Version - 1.2.0

• Adding moabb.evaluations.splitters.WithinSessionSplitter (#664 by Bruna Lopes_)

• Update version of pyRiemann to 0.7 (#671 by Gregoire Cattan)

• Add columns definitions in the datasets doc (#672 by Pierre Guetschel)

• Add ERP CORE datasets moabb.datasets.ErpCore2021 dataset (#627 by Taha Habib)

• Update paths of BIDS cache to better follow the standards. Cache created in previous MOABB versions should still be compatible (#707 by Pierre Guetschel)

• Fix Stieger2021 dataset bugs (#651 by Martin Wimpff)

• Unpinning major version Scikit-learn and numpy (#652 by Bruno Aristimunha)

• Replacing the func:numpy.string_ to func:numpy.bytes_ (#665 by Bruno Aristimunha)

• Fixing the set_download_dir that was not working when we tried to set the dir more than 10 times at the same time (#668 by Bruno Aristimunha)

• Creating stimulus channels in moabb.datasets.Zhou2016 and moabb.datasets.PhysionetMI to allow braindecode compatibility (#669 by Bruno Aristimunha)

• Improving the CI (#686 by Bruno Aristimunha)

• Making the download test work again (#693 by Bruno Aristimunha)

• Fix the EpochSelectChannel that caused incorrect channel selection in example (#685 by AFF)

• Fixing the logger on the Stieger2021 and Wang2016 dataset (#693 by Bruno Aristimunha)

• Change the way of creating the path to the folder (#697 by Sebastien Velut)

• Fixing bug with braindecode and moabb datasets EPFLP300 (#696 by Bruno Aristimunha)

• Fixing the dataset details for bids conversion (#698 by Bruno Aristimunha)

• Fixing unit issue and lack of montage with moabb.datasets.Rodrigues2017, moabb.datasets.Rodrigues2017, moabb.datasets.BaseCastillos2023, moabb.datasets.BaseCastillos2023, moabb.datasets.Huebner2018, moabb.datasets.Cattan2019_PHMD, moabb.datasets.Ofner2017 (#700 Bruno Aristimunha)

• Fix t-test permutation tests (#684 and #709 by Gregoire Cattan, Anton Andreev, Marco Congedo and Bruno Aristimunha)

• Removing the braindecode module from inside moabb (#666 by Bruno Aristimunha )

Version - 1.1.1

• Add possibility to use OptunaGridSearch (#630 by Igor Carrara)

• Add scripts to upload results on PapersWithCode (#561 by Pierre Guetschel)

• Centralize dataset summary tables in CSV files (#635 by Pierre Guetschel)

• Add new dataset moabb.datasets.Liu2024 dataset (#619 by Taha Habib)

• Add choice to choose the size of time window (by Sebastien Velut)

• Fix caching in the workflows (#632 by Pierre Guetschel)

• Include optuna as soft-dependency in the benchmark function and in the base of evaluation (#630 by Igor Carrara)

Version - 1.1.0

• Add cache option to the evaluation (#518 by Bruno Aristimunha)

• Option to interpolate channel in paradigms’ match_all method (#480 by Gregoire Cattan)

• Add leave k-Subjects out evaluations (#470 by Bruno Aristimunha)

• Update Braindecode dependency to 0.8 (#542 by Pierre Guetschel)

• Improve transform function of AugmentedDataset (#541 by Quentin Barthelemy)

• Add new paper results website (#556 by Bruno Aristimunha)

• Move cVEP common functions to moabb.datasets.utils (#564 #557 by Pierre Guetschel)

• Normalize c-VEP description tables (#562 #566 by Pierre Guetschel and Bruno Aristimunha)

• Update citation in README (#573 by Igor Carrara)

• Update pyRiemann dependency (#577 by Gregoire Cattan)

• Add resting stage Hinss2021 dataset (#580 by Gregoire Cattan and Yash Chauhan)

• Expose the learning rate parameter in the keras deep learning methods and optimize parameters (#589 and #592 by Bruno Aristimunha)

• Updating the braindecode pipelines for the new braindecode version 0.8.1 (#589 by Bruno Aristimunha)

• Add SSVEP and ERP paradigms to DL pipelines (#590 by Pierre Guetschel)

• Allow to pass a single pipeline file to benchmark (#591 by Pierre Guetschel)

• Add new dataset moabb.datasets.Stieger2021 (#604 by Reinmar Kobler and Bruno Aristimunha)

• Exposing the drop_rate for all the deep learning parameters (#592 by Bruno Aristimunha)

• Add new dataset moabb.datasets.Rodrigues2017 dataset (#602 by Gregoire Cattan and Pedro L. C. Rodrigues)

• Change unittest to pytest (#618 by Bruno Aristimunha)

• Remove tensorflow import warning (#622 by Bruno Aristimunha)

• Fix TRCA implementation for different stimulation freqs and for signal filtering (:gh:522 by Sylvain Chevallier)

• Fix saving to BIDS runs with a description string in their name (#530 by Pierre Guetschel)

• Fix import of keras BatchNormalization for TF 2.13 and higher (#544 by Brian Irvine)

• Fix the doc summary tables of moabb.datasets.Lee2019_SSVEP (#548 #547 #546 by Pierre Guetschel)

• Fix the doc summary for Castillos2023 dataset (#561 by Bruno Aristimunha)

• Fix format string receiving incorrect number of args in bids interface (#563 by Pierre Guetschel)

• Fix number of sessions in doc of moabb.datasets.Sosulski2019 (#565 by Pierre Guetschel)

• Fix code column of moabb.datasets.CastillosCVEP100 and moabb.datasets.CastillosCVEP100 (#567 by Pierre Guetschel)

• MAINT updating the packages pre-release (#578 by Bruno Aristimunha)

• Fix mne_bids version incompatibility with mne (#586 by Bruna Lopes)

• Updating the parameters of the SSVEP_TRCA method (#589 by Bruno Aristimunha)

• Fix and updating the parameters for the benchmark function (#588 by Bruno Aristimunha)

• Fix result table display (#599 by Sylvain Chevallier)

• Fix moabb.datasets.preprocessing.SetRawAnnotations setting incorrect annotations when the dataset’s interval does not start at 0 (#607 by Pierre Guetschel)

• Fix download link for GigaDB Cho2017 and Lee2019 datasets (#621 by Anton Andreev)

• None

Version - 1.0.0

• Adding extra thank you section in the documentation (#390 by Bruno Aristimunha)

• Adding new script to get the meta information of the datasets (#389 by Bruno Aristimunha)

• Fixing the dataset description based on the meta information (#389 and 398 by Bruno Aristimunha and Sara Sedlar)

• Adding second deployment of the documentation (#374 by Bruno Aristimunha)

• Adding Parallel evaluation for moabb.evaluations.WithinSessionEvaluation() , moabb.evaluations.CrossSessionEvaluation() (#364 by Bruno Aristimunha)

• Add example with VirtualReality BrainInvaders dataset (#393 by Gregoire Cattan and Pedro L. C. Rodrigues)

• Adding saving option for the models (#401 by Bruno Aristimunha and Igor Carrara)

• Adding example to load different type of models (#401 by Bruno Aristimunha and Igor Carrara)

• Add resting state paradigm with dataset and example (#400 by Gregoire Cattan and Pedro L. C. Rodrigues)

• Speeding the augmentation method by 400% with NumPy vectorization (#419 by Bruno Aristimunha)

• Add possibility to convert datasets to BIDS, plus example (PR #408, PR #391 by Pierre Guetschel and Bruno Aristimunha)

• Allow caching intermediate processing steps on disk, plus example (PR #408, issue #385 by Pierre Guetschel)

• Restructure the paradigms and datasets to move all preprocessing steps to moabb.datasets.preprocessing and as sklearn pipelines (PR #408 by Pierre Guetschel)

• Add moabb.paradigms.FixedIntervalWindowsProcessing() and moabb.paradigms.FilterBankFixedIntervalWindowsProcessing(), plus example (PR #408, issue #424 by Pierre Guetschel)

• Define moabb.paradigms.base.BaseProcessing(), common parent to moabb.paradigms.base.BaseParadigm() and moabb.paradigms.BaseFixedIntervalWindowsProcessing() (PR #408 by Pierre Guetschel)

• Allow passing a fixed processing pipeline to moabb.paradigms.base.BaseProcessing.get_data() and cache its result on disk (PR #408, issue #367 by Pierre Guetschel)

• Update moabb.datasets.fake.FakeDataset()’s code to be unique for each parameter combination (PR #408 by Pierre Guetschel)

• Systematically set the annotations when loading data, eventually using the stim channel (PR #408 by Pierre Guetschel)

• Allow moabb.datasets.utils.dataset_search() to search across paradigms paradigm=None (PR #408 by Pierre Guetschel)

• Improving the review processing with more pre-commit bots (#435 by Bruno Aristimunha)

• Add methods make_processing_pipelines and make_labels_pipeline to moabb.paradigms.base.BaseProcessing (#447 by Pierre Guetschel)

• Pipelines’ digests are now computed from the whole processing+classification pipeline (#447 by Pierre Guetschel)

• Update all dataset codes to remove white spaces and underscores (#448 by Pierre Guetschel)

• Add moabb.utils.depreciated_alias() decorator (#455 by Pierre Guetschel)

• Rename many dataset class names to standardize and deprecate old names (#455 by Pierre Guetschel)

• Change many dataset codes to match the class names (#455 by Pierre Guetschel)

• Add moabb.datasets.compound_dataset.utils.compound_dataset_list (#455 by Pierre Guetschel)

• Add c-VEP paradigm and Thielen2021 c-VEP dataset (#463 by Jordy Thielen)

• Add option to plot scores vertically. (#417 by Sara Sedlar)

• Change naming scheme for runs and sessions to align to BIDS standard (#471 by Pierre Guetschel)

• Increase the python version to 3.11 (#470 by Bruno Aristimunha)

• Add match_all method in paradigm to support CompoundDataset evaluation with MNE epochs (#473 by Gregoire Cattan)

• Automate setting of event_id in compound dataset and add data_origin information to the data (#475 by Gregoire Cattan)

• Add possibility of not saving the model (#489 by Igor Carrara)

• Add CVEP and BurstVEP dataset from Castillos from Toulouse lab (#531 by Sebastien Velut)

• Add c-VEP dataset from Thielen et al. 2015 (#557 by Jordy Thielen)

• Restore 3 subject from Cho2017 (#392 by Igor Carrara and Sylvain Chevallier)

• Correct downloading with VirtualReality BrainInvaders dataset (#393 by Gregoire Cattan)

• Rename event subtraction in moabb.datasets.Shin2017B() (#397 by Pierre Guetschel)

• Save parameters of moabb.datasets.PhysionetMI() (#403 by Pierre Guetschel)

• Fixing issue with parallel evaluation (#401 by Bruno Aristimunha and Igor Carrara)

• Fixing SSLError from BCI competition IV (#404 by Bruno Aristimunha)

• Fixing moabb.datasets.bnci.MNEBNCI.data_path() that returned the data itself instead of paths (#412 by Pierre Guetschel)

• Adding moabb.datasets.fake() in the init file to use in braindecode object (#414 by Bruno Aristimunha)

• Fixing the parallel download issue when the dataset have the same directory (#421 by Sara Sedlar)

• Fixing fixes the problem with the annotation loading for the P300 datasets Sosulski2019, Huebner2017 and Huebner2018 (#396 by Sara Sedlar)

• Removing the print in the dataset list (#423 by Bruno Aristimunha)

• Fixing bug in moabb.pipeline.utils_pytorch.BraindecodeDatasetLoader() where incorrect y was used in transform calls (#426 by Gabriel Schwartz)

• Fixing one test in moabb.pipeline.utils_pytorch.BraindecodeDatasetLoader() (#426 by Bruno Aristimunha)

• Fix moabb.benchmark() overwriting include_datasets list (#408 by Pierre Guetschel)

• Fix moabb.paradigms.base.BaseParadigm() using attributes before defining them (PR #408, issue #425 by Pierre Guetschel)

• Fix moabb.paradigms.FakeImageryParadigm(), moabb.paradigms.FakeP300Paradigm() and moabb.paradigms.FakeSSVEPParadigm() is_valid methods to only accept the correct datasets (PR #408 by Pierre Guetschel)

• Fix dataset_list construction, which could be empty due to bad import order (PR #449 by Thomas Moreau).

• Fixing dataset downloader from servers with non-http (PR #433 by Sara Sedlar)

• Fix dataset_list to include deprecated datasets (PR #464 by Bruno Aristimunha)

• Fixed bug in moabb.analysis.results.get_string_rep() to handle addresses such as 0x__0A as well (PR #468 by Anton Andreev)

• Moving the moabb.evualation.grid_search() to inside the base evaluation (#487 by Bruno Aristimunha)

• Removing joblib Parallel (#488 by Igor Carrara)

• Fix case when events specified via raw.annotations but no events (#491 by Pierre Guetschel)

• Fix bug in downloading Shin2017A dataset (#493 by Igor Carrara)

• Fix the cropped option in the dataset preprocessing (#502 by Bruno Aristimunha)

• Fix bug in moabb.datasets.utils.dataset_search() with missing cvep paradigm (#557 by Jordy Thielen)

• Fix mistakes in moabb.datasets.thielen2021() considering wrong docs and hardcoded trial stim channel (#557 by Jordy Thielen)

• None

Version - 0.5.0

• Speeding the augmentation model (#365 by Bruno Aristimunha)

• Add VirtualReality BrainInvaders dataset (#358 by Gregoire Cattan)

• Switch to python-3.8, update dependencies, fix code link in doc, add code coverage (#315 by Sylvain Chevallier)

• Adding a comprehensive benchmarking function (#264 by Divyesh Narayanan and Sylvain Chevallier)

• Add meta-information for datasets in documentation (#317 by Bruno Aristimunha)

• Add GridSearchCV for different evaluation procedure (#319 by Igor Carrara)

• Add new tutorial to benchmark with GridSearchCV (#323 by Igor Carrara)

• Add six deep learning models (Tensorflow), and build a tutorial to show to use the deep learning model (#326 by Igor Carrara, Bruno Aristimunha and Sylvain Chevallier)

• Add a augmentation model to the pipeline (#326 by Igor Carrara)

• Add BrainDecode example (#340 by Igor Carrara and Bruno Aristimunha)

• Add Google Analytics to the documentation (#335 by Bruno Aristimunha)

• Add support to Braindecode classifier (#328 by Bruno Aristimunha)

• Add CodeCarbon to track emission CO₂ (#350 by Igor Carrara, Bruno Aristimunha and Sylvain Chevallier)

• Add CodeCarbon example (#356 by Igor Carrara and Bruno Aristimunha)

• Add MsetCCA method for SSVEP classification, parametrise CCA n_components in CCA based methods (#359 by Emmanuel Kalunga and Sylvain Chevallier)

• Set epochs’ metadata field in get_data (#371 by Pierre Guetschel)

• Add possibility to use transformers to apply fixed pre-processings before evaluations (#372 by Pierre Guetschel)

• Add seed parameter to FakeDataset (#372 by Pierre Guetschel)

• Fix circular import with braindecode (#363 by Bruno Aristimunha)

• Fix bug for MotorImagery when we handle all events (#327 by Igor Carrara)

• Fixing CI to handle with new deep learning dependencies (#332 and #326 by Igor Carrara, Bruno Aristimunha and Sylvain Chevallier)

• Correct CI error due to isort (#330 by Bruno Aristimunha)

• Restricting Python <= 3.11 version and adding tensorflow, keras, scikeras, braindecode, skorch and torch, as optional dependence (#329 by Bruno Aristimunha)

• Fix numpy variable to handle with the new version of python (#324 by Bruno Aristimunha)

• Correct CI error due to black (#292 by Sylvain Chevallier)

• Preload Schirrmeister2017 raw files (#290 by Pierre Guetschel)

• Incorrect event assignation for Lee2019 in MNE >= 1.0.0 (#298 by Sylvain Chevallier)

• Correct usage of name simplification function in analyze (#306 by Divyesh Narayanan)

• Fix downloading path issue for Weibo2014 and Zhou2016, numy error in DemonsP300 (#315 by Sylvain Chevallier)

• Fix unzip error for Huebner2017 and Huebner2018 (#318 by Sylvain Chevallier)

• Fix n_classes when events set to None (#337 by Igor Carrara and Sylvain Chevallier)

• Change n_jobs=-1 to self.n_jobs in GridSearch (#344 by Igor Carrara)

• Fix dropped epochs issue (#371 by Pierre Guetschel)

• Fix redundancy website issue (#372 by Bruno Aristimunha)

• None

Version - 0.4.6

• Add P300 BrainInvaders datasets (#283 by Sylvain Chevallier)

• Add explicit warning when lambda function are used to parametrize pipelines (#278 by Jan Sosulski)

• Correct default path for ERP visualization (#279 by Jan Sosulski)

• Correct documentation (#282 and #284 by Jan Sosulski)

Version - 0.4.5

• Progress bars, pooch, tqdm (#258 by Divyesh Narayanan and Sylvain Chevallier)

• Adding test and example for set_download_dir (#249 by Divyesh Narayanan)

• Update to newer version of Schirrmeister2017 dataset (#265 by Robin Schirrmeister)

• Adding Huebner2017 and Huebner2018 P300 datasets (#260 by Jan Sosulski)

• Adding Sosulski2019 auditory P300 datasets (#266 by Jan Sosulski)

• New script to visualize ERP on all datasets, as a sanity check (#261 by Jan Sosulski)

• Removing dependency on mne method for PhysionetMI data downloading, renaming runs (#257 by Divyesh Narayanan)

• Correcting events management in Schirrmeister2017, renaming session and run (#255 by Pierre Guetschel and Sylvain Chevallier)

• Switch session and runs in MAMEM1, 2 and 3 to avoid error in WithinSessionEvaluation (#256 by Sylvain Chevallier)

• Correct doctstrings for the documentation, including Lee2017 (#256 by Sylvain Chevallier)

Version - 0.4.4

• Add TRCA algorithm for SSVEP (#238 by Ludovic Darmet)

• Remove unused argument from dataset_search (#243 by Divyesh Narayanan)

• Remove MNE call to _fetch_dataset and use MOABB _fetch_file (#235 by Jan Sosulski)

• Correct doc formatting (#232 by Sylvain Chevallier)

• Minimum supported Python version is now 3.7

• MOABB now depends on scikit-learn >= 1.0

Version - 0.4.3

• Rewrite Lee2019 to add P300 and SSVEP datasets (#217 by Pierre Guetschel)

• Avoid information leakage for MNE Epochs pipelines in evaluation (#222 by Sylvain Chevallier)

• Correct error in set_download_dir (#225 by Sylvain Chevallier)

• Ensure that channel order is consistent across dataset when channel argument is specified in paradigm (#229 by Sylvain Chevallier)

• ch_names argument added to init of moabb.datasets.fake.FakeDataset (#229 by Sylvain Chevallier)

Version - 0.4.2

• None

• Correct error when downloading Weibo dataset (#212 by Sylvain Chevallier)

• None

Version - 0.4.1

• None

• Correct path error for first time launch (#204 by Sylvain Chevallier)

• Fix optional dependencies issues for PyPi (#205 by Sylvain Chevallier)

• Remove update_path on all datasets, update_path parameter in dataset.data_path() is deprecated (#207 by Sylvain Chevallier)

Version - 0.4.0

• Implementation for learning curves (#155 by Jan Sosulski)

• Adding Neiry Demons P300 dataset (#156 by Vladislav Goncharenko)

• Coloredlogs removal (#163 by Vladislav Goncharenko)

• Update for README (#164 by Vladislav Goncharenko and Sylvain Chevallier)

• Test all relevant python versions in Github Actions CI (#167 by Vladislav Goncharenko)

• Adding motor imagery part of the Lee2019 dataset (#170 by Ali Abdul Hussain)

• CI: deploy docs from CI pipeline (#124 by Erik Bjäreholt, Divyesh Narayanan and Sylvain Chevallier)

• Remove dependencies: WFDB and pyunpack (#180 and #188 by Sylvain Chevallier)

• Add support for FigShare API (#188 by Sylvain Chevallier)

• New download backend function relying on Pooch, handling FTP, HTTP and HTTPS (#188 by Sylvain Chevallier)

• Complete rework of examples and tutorial (#188 by Sylvain Chevallier)

• Change default storage location for results: instead of moabb source code directory it is now stored in mne_data (#188 by Sylvain Chevallier)

• Major update of test (#188 by Sylvain Chevallier)

• Adding troubleshooting and badges in README (#189 by Jan Sosulski and Sylvain Chevallier)

• Use MNE epoch in evaluation (#192 by Sylvain Chevallier)

• Allow changing of storage location (#192 by Divyesh Narayanan and Sylvain Chevallier)

• Deploy docs on moabb.github.io (#196 by Sylvain Chevallier)

• Broadening subject_list type for moabb.datasets.BaseDataset() (#198 by Sylvain Chevallier)

• Adding this what’s new (#200 by Sylvain Chevallier)

• Improving cache usage and save computation time in CI (#200 by Sylvain Chevallier)

• Rewrite Lee2019 to add P300 and SSVEP datasets (#217 by Pierre Guetschel)

• Restore basic logging (#177 by Jan Sosulski)

• Correct wrong type of results dataframe columns (#188 by Sylvain Chevallier)

• Add accept arg to acknowledge licence for moabb.datasets.Shin2017A() and moabb.datasets.Shin2017B() (#201 by Sylvain Chevallier)

• Drop update_path from moabb.download.data_path and moabb.download.data_dl

Version 0.3.0

• Expose sklearn error_score parameter (#70 by Jan Sosulski)

• Adds a unit_factor attribute to base_paradigms (#72 by Jan Sosulski)

• Allow event lists in P300 paradigm (#83 by Jan Sosulski)

• Return epochs instead of np.ndarray in process_raw (#86 by Jan Sosulski)

• Set path for hdf5 files (#92 by Jan Sosulski)

• Update to MNE 0.21 (#101 by Ramiro Gatti and Sylvain Chevallier)

• Adding a baseline correction (#115 by Ramiro Gatti)

• Adding SSVEP datasets: MAMEM1, MAMEM2, MAMEM3, Nakanishi2015, Wang2016, (#118 by Sylvain Chevallier, Quentin Barthelemy, and Divyesh Narayanan)

• Switch to GitHub Actions (#124 by Erik Bjäreholt)

• Allow recording of additional scores/parameters/metrics in evaluation (#127 and #128 by Jan Sosulski)

• Fix Ofner2017 and PhysionetMI annotations (#135 by Ali Abdul Hussain)

• Adding Graz workshop tutorials (#130 and #137 by Sylvain Chevallier and Lucas Custódio)

• Adding pre-commit configuration using isort, black and flake8 (#140 by Vladislav Goncharenko)

• style: format Python code with black (#147 by Erik Bjäreholt)

• Switching to Poetry dependency management (#150 by Vladislav Goncharenko)

• Using Prettier to format md and yml files (#151 by Vladislav Goncharenko)

• Use stim_channels or check annotation when loading files in Paradigm (#72 by Jan Sosulski)

• Correct MNE issues (#76 by Sylvain Chevallier)

• Fix capitalization in channel names of cho dataset (#90 by Jan Sosulski)

• Correct failing CI tests (#100 by Sylvain Chevallier)

• Fix EPFL dataset flat signal sections and wrong scaling (#104 and #96 by Jan Sosulski)

• Fix schirrmeister dataset for Python3.8 (#105 by Robin Schirrmeister)

• Correct event detection problem and duplicate event error (#106 by Sylvain Chevallier)

• Fix channel selection in paradigm (#108 by Sylvain Chevallier)

• Fix upperlimb Ofner2017 error and gdf import problem (#111 and #110 by Sylvain Chevallier)

• Fix event_id in events_from_annotations missed (#112 by Ramiro Gatti)

• Fix h5py>=3.0 compatibility issue (#138 by Mohammad Mostafa Farzan)

• Python 2 support removal (#148 by Vladislav Goncharenko)

• Travis-ci config removal (#149 by Vladislav Goncharenko)

• None

Version 0.2.1

• Add Tikhonov regularized CSP in moabb.pipelines.csp from the paper (#60 by Vinay Jayaram)

• update to MNE version 0.19 (#73 by Jan Sosulski)

• Improve doc building in CI (#60 by Sylvain Chevallier)

• Update GigaDB Cho2017 URL (Pedro L. C. Rodrigues and Vinay Jayaram)

• Fix braininvaders ERP data (Pedro L. C. Rodrigues)

• Replace MNE read_montage with make_standard_montage (Jan Sosulski)

• Correct Flake and PEP8 error (Sylvain Chevallier)

• None

Version 0.2.0

• MOABB corresponding to the paper version by Vinay Jayaram and Alexandre Barachant

• Creating P300 paradigm and BNCI datasets (#53 by Pedro L. C. Rodrigues)

• Adding EPFL P300 dataset (#56 by Pedro L. C. Rodrigues)

• Adding BrainInvaders P300 dataset (#57 by Pedro L. C. Rodrigues)

• Creating SSVEP paradigm and SSVEPExo dataset (#59 by Sylvain Chevallier)

• None

• None