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README.md

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+# **🧠MIRepNet**
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 ## 📌 Abstract
 Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. Recent EEG foundation models aim to learn generalized representations across diverse BCI paradigms. However, these approaches overlook fundamental paradigm-specific neurophysiological distinctions, limiting their generalization ability. Importantly, in practical BCI deployments, the specific paradigm such as motor imagery (MI) for stroke rehabilitation or assistive robotics, is generally determined prior to data acquisition. To address these issues, we propose MIRepNet, the first EEG foundation model explicitly tailored for the MI paradigm. MIRepNet comprises a high-quality EEG preprocessing pipeline incorporating a neurophysiologically-informed channel template, adaptable to EEG headsets with arbitrary electrode configurations. Furthermore, we introduce a hybrid pretraining strategy that combines self-supervised masked token reconstruction and supervised MI classification, facilitating rapid adaptation and accurate decoding on novel downstream MI tasks with fewer than 30 trials per class. Extensive evaluations across five public MI datasets demonstrate that MIRepNet consistently achieves state-of-the-art performance, significantly outperforming both specialized and generalized EEG models.
 
-![RepMI](asset/RepMI.jpg)
 
-## 🚀  Contributions
-- 🧩 We introduce MIRepNet, the first paradigm-specific foundation model specifically tailored explicitly for MI tasks. By capturing MI-specific neurophysiological features, MIRepNet effectively learns generalizable representations for MI decoding.
-- 🛠️ We propose a high-quality EEG preprocessing pipeline comprising subject screening, a unified channel-template-based spatial alignment, frequency filtering, temporal resampling, and distribution alignment. This approach addresses challenges arising from heterogeneous EEG headset configurations, ensuring data consistency across diverse datasets.
-- 🎯 We develop an efficient pretraining approach combining masked token reconstruction and supervised MI classification. This strategy enables the model to acquire robust, generalizable temporal-spatial EEG representations.
-- 📊 Extensive experiments on five public MI datasets including 47 downstream subjects demonstrate that RepMI achieves state-of-the-art decoding accuracy. Moreover, RepMI requires significantly fewer calibration trials (fewer than 30 trials per class) and rapidly converges in a few epochs, highlighting its practical utility and effectiveness.