Package: metabci
MetaBCIWelcome!MetaBCI is an open-source platform for non-invasive brain computer interface, which has 3 main parts:brainda: for importing dataset, pre-processing EEG data and implementing EEG decoding algorithms.brainflow: a high speed EEG online data processing framework.brainstim: a simple and efficient BCI experiment paradigms design module.This is the first release of MetaBCI, our team will continue to maintain the repository. If you need the handbook of this repository, please contact us by sending email toTBC_TJU_2022@163.comwith the following information:Name of your teamleaderName of your university(or organization)We will send you a copy of the handbook as soon as we receive your information.MetaBCIWelcome!What are we doing?The problemThe solutionFeaturesInstallationWho are we?What do we need?ContributingLicenseContactAcknowledgementsWhat are we doing?The problemBCI datasets come in different formats and standardsIt's tedious to figure out the details of the dataLack of python implementations of modern decoding algorithmsIt's not an easy thing to perform BCI experiments especially for the online ones.If someone new to the BCI wants to do some interesting research, most of their time would be spent on preprocessing the data, reproducing the algorithm in the paper, and also find it difficult to bring the algorithms into BCI experiments.The solutionThe Meta-BCI will:Allow users to load the data easily without knowing the detailsProvide flexible hook functions to control the preprocessing flowProvide the latest decoding algorithmsProvide the experiment UI for different paradigms (e.g. MI, P300 and SSVEP)Provide the online data acquiring pipeline.Allow users to bring their pre-trained models to the online decoding pipeline.The goal of the Meta-BCI is to make researchers focus on improving their own BCI algorithms and performing their experiments without wasting too much time on preliminary preparations.FeaturesImprovements to MOABB APIsadd hook functions to control the preprocessing flow more easilyuse joblib to accelerate the data loadingadd proxy options for network conneciton issuesadd more information in the meta of dataother small changesSupported DatasetsMI DatasetsAlexMIBNCI2014001, BNCI2014004PhysionetMI, PhysionetMECho2017MunichMISchirrmeister2017Weibo2014Zhou2016SSVEP DatasetsNakanishi2015Wang2016BETAImplemented BCI algorithmsDecomposition MethodsSPoC, CSP, MultiCSP and FBCSPCCA, itCCA, MsCCA, ExtendCCA, ttCCA, MsetCCA, MsetCCA-R, TRCA, TRCA-R, SSCOR and TDCADSPManifold LearningBasic Riemannian Geometry operationsAlignment methodsRiemann Procustes AnalysisDeep LearningShallowConvNetEEGNetConvCAGuneyNetTransfer LearningMEKTLSTInstallationClone the repogitclonehttps://github.com/TBC-TJU/MetaBCI.gitChange to the project directorycdMetaBCIInstall all requirementspipinstall-rrequirements.txtInstall brainda package with the editable modepipinstall-e.Who are we?The MetaBCI project is carried out by researchers fromAcademy of Medical Engineering and Translational Medicine, Tianjin University, ChinaTianjin Brain Center, ChinaWhat do we need?You! In whatever way you can help.We need expertise in programming, user experience, software sustainability, documentation and technical writing and project management.We'd love your feedback along the way.ContributingContributions are what make the open source community such an amazing place to be learn, inspire, and create.Any contributions you make are greatly appreciated. Especially welcome to submit BCI algorithms.Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestLicenseDistributed under the GNU General Public License v2.0 License. SeeLICENSEfor more information.ContactEmail:TBC_TJU_2022@163.comAcknowledgementsMNEMOABBpyRiemannTRCA/eTRCAEEGNetRPAMEKT