{"url":"/task/motor-imagery","name":"Motor Imagery","slug":"motor-imagery","description_markdown":"Classification of examples recorded under the Motor Imagery paradigm, as part of Brain-Computer Interfaces (BCI).\r\n\r\nA number of motor imagery datasets can be downloaded using the MOABB library: [motor imagery datasets list](http://moabb.neurotechx.com/docs/dataset_summary.html#motor-imagery)","categories":[{"name":"Medical","url":"/area/medical"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":252,"papers_with_code":81,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":1,"parent_tasks":1},"benchmarks":[],"datasets":[],"subtasks":[{"url":"/task/within-session-motor-imagery","name":"Within-Session Motor Imagery"}],"parent_tasks":[{"url":"/task/brain-computer-interface","name":"Brain Computer Interface"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":81,"tagged_in_all":252,"items":[{"url":"/paper/eegnet-a-compact-convolutional-network-for","title":"EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces","date":"2016-11-23","arxiv_id":"1611.08024","repositories_listed":11,"syntology":{"n":8,"n_ran":1,"n_unverified":7,"n_pointer_only":1}},{"url":"/paper/time-space-frequency-feature-fusion-for-3","title":"Time-space-frequency feature Fusion for 3-channel motor imagery classification","date":"2023-04-04","arxiv_id":"2304.01461","repositories_listed":3,"syntology":null},{"url":"/paper/lmda-net-a-lightweight-multi-dimensional","title":"LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability","date":"2023-03-29","arxiv_id":"2303.16407","repositories_listed":3,"syntology":null},{"url":"/paper/priming-cross-session-motor-imagery","title":"Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework","date":"2022-02-19","arxiv_id":"2202.09559","repositories_listed":3,"syntology":null},{"url":"/paper/eeg-motor-imagery-decoding-a-framework-for","title":"EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms","date":"2023-10-17","arxiv_id":"2310.11198","repositories_listed":2,"syntology":null},{"url":"/paper/physics-inform-attention-temporal","title":"Physics-inform attention temporal convolutional network for EEG-based motor imagery classification","date":"2022-08-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/cnn-based-approaches-for-cross-subject","title":"CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet","date":"2021-05-17","arxiv_id":"2105.07917","repositories_listed":2,"syntology":null},{"url":"/paper/fast-and-accurate-multiclass-inference-for-mi","title":"Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features","date":"2018-06-18","arxiv_id":"1806.06823","repositories_listed":2,"syntology":null},{"url":"/paper/converting-your-thoughts-to-texts-enabling","title":"Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals","date":"2017-09-26","arxiv_id":"1709.08820","repositories_listed":2,"syntology":null},{"url":"/paper/neuroxai-adaptive-robust-explainable","title":"NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application","date":"2025-09-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dbconformer-dual-branch-convolutional","title":"DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding","date":"2025-06-26","arxiv_id":"2506.21140","repositories_listed":1,"syntology":null},{"url":"/paper/agtcnet-a-graph-temporal-approach-for","title":"AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification","date":"2025-06-26","arxiv_id":"2506.21338","repositories_listed":1,"syntology":null},{"url":"/paper/tcanet-a-temporal-convolutional-attention","title":"TCANet: A Temporal Convolutional Attention Network for Motor Imagery EEG Decoding","date":"2025-06-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dataset-combining-eeg-eye-tracking-and-high","title":"Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","date":"2025-06-09","arxiv_id":"2506.07488","repositories_listed":1,"syntology":null},{"url":"/paper/multi-scale-convolutional-transformer-network","title":"Multi-scale convolutional transformer network for motor imagery brain-computer interface","date":"2025-04-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-contrastive-network-mcnet-for","title":"MVCNet: Multi-View Contrastive Network for Motor Imagery Classification","date":"2025-02-18","arxiv_id":"2502.17482","repositories_listed":1,"syntology":null},{"url":"/paper/minima-possible-weights-a-homogenous-deep","title":"Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery Classification","date":"2025-02-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-case-for-cleaner-biosignals-high-fidelity","title":"The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG","date":"2025-02-10","arxiv_id":"2502.17462","repositories_listed":1,"syntology":{"n":8,"n_ran":0,"n_unverified":8,"n_pointer_only":0}},{"url":"/paper/fine-tuning-strategies-for-continual-online","title":"Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study","date":"2025-02-05","arxiv_id":"2502.06828","repositories_listed":1,"syntology":null},{"url":"/paper/motor-imagery-classification-for-asynchronous","title":"Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces","date":"2024-12-12","arxiv_id":"2412.09006","repositories_listed":1,"syntology":null},{"url":"/paper/t-time-test-time-information-maximization","title":"T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs","date":"2024-12-10","arxiv_id":"2412.07228","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/federated-motor-imagery-classification-for","title":"Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces","date":"2024-12-02","arxiv_id":"2412.01079","repositories_listed":1,"syntology":null},{"url":"/paper/the-more-the-better-evaluating-the-role-of","title":"The more, the better? Evaluating the role of EEG preprocessing for deep learning applications","date":"2024-11-27","arxiv_id":"2411.18392","repositories_listed":1,"syntology":null},{"url":"/paper/eeg-dcnet-a-fast-and-accurate-mi-eeg-dilated-1","title":"EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method","date":"2024-11-12","arxiv_id":"2411.17705","repositories_listed":1,"syntology":null},{"url":"/paper/alignment-based-adversarial-training-abat-for","title":"Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs","date":"2024-11-04","arxiv_id":"2411.02094","repositories_listed":1,"syntology":null},{"url":"/paper/mixnet-joining-force-of-classical-and-modern","title":"MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification","date":"2024-09-06","arxiv_id":"2409.04104","repositories_listed":1,"syntology":null},{"url":"/paper/ctnet-a-convolutional-transformer-network-for","title":"CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification","date":"2024-08-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/feature-interpretability-in-bcis-exploring","title":"Feature interpretability in BCIs: exploring the role of network lateralization","date":"2024-07-16","arxiv_id":"2407.11617","repositories_listed":1,"syntology":null},{"url":"/paper/subject-adaptive-transfer-learning-using","title":"Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification","date":"2024-05-17","arxiv_id":"2405.19346","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-spatial-domain-explanations-in","title":"Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance","date":"2024-05-02","arxiv_id":"2405.01277","repositories_listed":1,"syntology":null}],"syntology_records":3,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}