# Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/2412.09006 · HTML: https://mlsanity.com/paper/2412.09006/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization](https://mlsanity.com/paper/2110.10832/) (2021)
- [Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach](https://mlsanity.com/paper/1808.05464/) (2018)
- [Self-supervised EEG Representation Learning for Automatic Sleep Staging](https://mlsanity.com/paper/2110.15278/) (2021)
- [Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline](https://mlsanity.com/paper/2007.03746/) (2020)
- [T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs](https://mlsanity.com/paper/2412.07228/) (2024)
- [Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals](https://mlsanity.com/paper/1709.08820/) (2017)
- [Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning](https://mlsanity.com/paper/1808.10852/) (2018)

## Built on by (most cited first, 5 of 5)

- [Imagined Speech State Classification for Robust Brain-Computer Interface](https://mlsanity.com/paper/2412.12215/) (2024)
- [Subject-Independent Imagined Speech Detection via Cross-Subject Generalization and Calibration](https://mlsanity.com/paper/2511.13739/) (2025)
- [DANCE: Detect and Classify Events in EEG](https://mlsanity.com/paper/2605.10688/) (2026)
- [Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning](https://mlsanity.com/paper/2610.16921/) (2026)
- [NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection](https://mlsanity.com/paper/2610.20349/) (2026)

## Abstract (arXiv)

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%.