{"id":24189404,"url":"https://github.com/okbalefthanded/aawedha","last_synced_at":"2025-06-26T18:32:53.472Z","repository":{"id":133913547,"uuid":"169551695","full_name":"okbalefthanded/aawedha","owner":"okbalefthanded","description":"Deep Learning toolbox for EEG based Brain-Computer Interface signals decoding and benchmarking","archived":false,"fork":false,"pushed_at":"2024-03-20T22:43:32.000Z","size":1007,"stargazers_count":3,"open_issues_count":12,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-03T03:24:18.702Z","etag":null,"topics":["benchmark","brain-computer-interface","deep-learning","eeg","erp","machine-learning","motor-imagery","ssvep"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/okbalefthanded.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-02-07T10:07:17.000Z","updated_at":"2024-04-23T13:02:22.000Z","dependencies_parsed_at":"2025-01-13T14:34:08.683Z","dependency_job_id":"75269ecf-fcff-431b-834b-fd5358c7863f","html_url":"https://github.com/okbalefthanded/aawedha","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/okbalefthanded/aawedha","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okbalefthanded%2Faawedha","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okbalefthanded%2Faawedha/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okbalefthanded%2Faawedha/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okbalefthanded%2Faawedha/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/okbalefthanded","download_url":"https://codeload.github.com/okbalefthanded/aawedha/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okbalefthanded%2Faawedha/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262123012,"owners_count":23262513,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["benchmark","brain-computer-interface","deep-learning","eeg","erp","machine-learning","motor-imagery","ssvep"],"created_at":"2025-01-13T14:29:33.340Z","updated_at":"2025-06-26T18:32:53.385Z","avatar_url":"https://github.com/okbalefthanded.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Aawedha\n\n***Aawedha*** (*عاودها* means repeate it or do it again in Algerian arabic) is a deep learning learning package based on [Keras](https://www.tensorflow.org/guide/keras/overview) with [Tensorflow](https://www.tensorflow.org/guide) backend, for EEG based Brain-Computer Interface (BCI) decoding research and application.\n\nCompatible with **Python 3.6 and above**\n\n---\n\n## Motivation\n\nThe main goal for this package is to provide a flexible and complete analysis and benchmarking tool for Deep Learning research in BCI. \n\n---\n\n## Features\nAawedha provides a complete set of operations from raw data preprocessing to model evaluation and results visualization. A regular workflow using this package consists of 5 instructions:\n- Create a dataset: preprocess raw data to create epoched EEG trials (run once)\n- Define an Evaluation : Single subject or Cross Subject analysis with the data and model.\n- Generate a random data split.\n- Run evaluation : train and test model.\n- Visualize the results and what the model has learnt.\n\nThe tables below show the available datasets and models, for a detailed tutorial on running the evaluations follow the colaboratory notebook in the examples folder. \n### Data\n\n|   Datasets               | Paradigm      | Participants(subjects)  | \n| -------------            |:-------------:| :-----:|\n| [BCI Competetion IV 2a](http://www.bbci.de/competition/iv/)    | Motor Imagery | 9      | \n| [Exoskleton](https://github.com/sylvchev/dataset-ssvep-exoskeleton)               | SSVEP         | 12     |      \n| [Freiburg Online ERP](https://zenodo.org/record/192684)            | ERP     |     13 | \n| [Inria ERN](https://www.kaggle.com/c/inria-bci-challenge)            | ErrP      |   26     |\n| [Laresi Hyrbid]()            | Hybrid ERP/SSVEP      |    1    |\n| [Physionet_MI](https://physionet.org/content/eegmmidb/1.0.0/)            | Motor Imagery      |    109    |\n| [San Diego](ftp://sccn.ucsd.edu/pub/cca_ssvep)            | SSVEP      |   10     |\n| [Tsinghua](http://bci.med.tsinghua.edu.cn/download.html)            | SSVEP     |   35     |       \n\n### Deep Learning Models\n\n|   Title       | Paradigm      | Architecture  |\n| ------------- |:-------------:| -----:|\n| [EEGNET](https://github.com/vlawhern/arl-eegmodels)       | Motor Imagery / ERP/Errp | ConvNet |\n| [EEGNet SSVEP](https://github.com/vlawhern/arl-eegmodels)  | SSVEP         |   ConvNet |\n| [DeepConvNet/ ShallowConvNet](https://github.com/TNTLFreiburg/braindecode) | Motor Imagery / ERP/Errp      |    ConvNet |\n| [1DCSU](https://arxiv.org/abs/1805.04157)       | SSVEP | ConvNet |\n| [PodNet](http://dro.dur.ac.uk/27626/)      | SSVEP | ConvNet |\n| [KoreaU CNN](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172578)       | SSVEP | ConvNet |\n| [Xu_Jiang CNN](https://ieeexplore.ieee.org/document/8708243)       | SSVEP | ConvNet |\n---\n\n## Installation\n\nFirst, clone Aawedha using git:\n```\ngit clone https://github.com/okbalefthanded/aawedha.git\n```\nThen, cd to the Aawedha folder, install requirements using pip then proceed to package setup:\n```\ncd aawedha\n\npip install -r requirements.txt\n\npython setup.py install\n```\n\n---\n\n## Usage\n```\nFollow the colab notebooks in /examples\n```\n---\n\n## Citation\n\n---\n\n## Acknowledgment \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fokbalefthanded%2Faawedha","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fokbalefthanded%2Faawedha","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fokbalefthanded%2Faawedha/lists"}