Motor Imagery

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Most implemented papers archive 2025-07-28

30 shown of 81 papers with code (252 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
  23 Nov 2016 11 repositories listed Syntology ran 1 of 8 samples · 7 unverified · 1 pointer-only (licence)
  We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.
• Time-space-frequency feature Fusion for 3-channel motor imagery classification
  4 Apr 2023 3 repositories listed
  TSFF-Net comprises four main components: time-frequency representation, time-frequency feature extraction, time-space feature extraction, and feature fusion and classification.
• LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
  29 Mar 2023 3 repositories listed
  By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions,…
• Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
  19 Feb 2022 3 repositories listed
  Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15.
• EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms
  17 Oct 2023 2 repositories listed
  The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding.
• Physics-inform attention temporal convolutional network for EEG-based motor imagery classification
  1 Aug 2022 2 repositories listed
  In this paper, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification.
• CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet
  17 May 2021 2 repositories listed
  In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance.
• Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
  18 Jun 2018 2 repositories listed
  Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems.
• Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
  26 Sep 2017 2 repositories listed
  An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and…
• NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application
  12 Sep 2025 1 repository listed
  The advantages of the proposed method include enhanced performance, robustness over varying data paradigms and the type of prediction model.
• DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
  26 Jun 2025 1 repository listed
  It integrates a temporal Conformer to model long-range temporal dependencies and a spatial Conformer to extract inter-channel interactions, capturing both temporal dynamics and spatial patterns in EEG signals.
• AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification
  26 Jun 2025 1 repository listed
  Brain-computer interface (BCI) technology utilizing electroencephalography (EEG) marks a transformative innovation, empowering motor-impaired individuals to engage with their environment on equal footing.
• TCANet: A Temporal Convolutional Attention Network for Motor Imagery EEG Decoding
  15 Jun 2025 1 repository listed
  This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features.
• Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
  9 Jun 2025 1 repository listed
  In Brain-Computer Interface (BCI) research, the detailed study of blinks is crucial.
• Multi-scale convolutional transformer network for motor imagery brain-computer interface
  15 Apr 2025 1 repository listed
  However, traditional CNN-based methods face challenges such as individual variability in EEG signals and the limited receptive fields of CNNs.
• MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
  18 Feb 2025 1 repository listed
  Two contrastive modules are further introduced: a cross-view contrastive module that enforces consistency of original and augmented views, and a cross-model contrastive module that aligns features extracted from both…
• Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery Classification
  18 Feb 2025 1 repository listed
  We evaluated MPW against various ensemble learning and test-time adaptation methods using two benchmark datasets: BCI Competition IV Dataset 2a and PhysionetMI.
• The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG
  10 Feb 2025 1 repository listed Syntology ran 0 of 8 samples · 8 unverified
  BrainCodec also achieves up to a 64x compression on iEEG and EEG without a notable decrease in quality.
• Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study
  5 Feb 2025 1 repository listed
  This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) decoding within a causal setting involving a large user group and…
• Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces
  12 Dec 2024 1 repository listed
  Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts.
• T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
  10 Dec 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible.
• Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
  2 Dec 2024 1 repository listed
  Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration.
• The more, the better? Evaluating the role of EEG preprocessing for deep learning applications
  27 Nov 2024 1 repository listed
  The last decade has witnessed a notable surge in deep learning applications for the analysis of electroencephalography (EEG) data, thanks to its demonstrated superiority over conventional statistical techniques.
• EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
  12 Nov 2024 1 repository listed
  We incorporate the 1×1 convolutional layer and utilize the multi-branch parallel atrous convolutional architecture in EEG-DCNet to capture the highly nonlinear characteristics and multi-scale features of the EEG signals.
• Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs
  4 Nov 2024 1 repository listed
  Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary.
• MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification
  6 Sep 2024 1 repository listed
  Recent advances in deep learning (DL) have significantly impacted motor imagery (MI)-based brain-computer interface (BCI) systems, enhancing the decoding of electroencephalography (EEG) signals.
• CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
  30 Aug 2024 1 repository listed
  In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.
• Feature interpretability in BCIs: exploring the role of network lateralization
  16 Jul 2024 1 repository listed
  In this study, we investigated the interpretability of features derived from brain network lateralization, benchmarking against widely used techniques like power spectrum density (PSD), common spatial pattern (CSP), and…
• Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification
  17 May 2024 1 repository listed
  Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.
• Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance
  2 May 2024 1 repository listed
  This work investigates the efficacy of different deep learning and Riemannian geometry-based classification models in the context of motor imagery (MI) based BCI using electroencephalography (EEG).

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.