MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery
  Classification
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. This paper proposes MIRepNet, the first EEG foundation model
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 demonstrated that MIRepNet consistently achieved
state-of-the-art performance, significantly outperforming both specialized and
generalized EEG models. Our code will be available on
GitHubhttps://github.com/staraink/MIRepNet.
6889d34a31e1218a089282df
Dingkun Liu
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2025-07-30T14:18:26.400Z
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Dingkun Liu
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DiMaria0817
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Zhu Chen
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2025-07-31T08:15:57.839Z
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zhuchen
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starself
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Jingwei Luo
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2025-07-30T14:18:24.838Z
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JingweiLuo
user
JingweiLuo92
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Shijie Lian
claimed_verified
2025-08-13T07:29:45.865Z
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Shijie Lian
user
LiamLian0727
6889d34a31e1218a089282e3
Dongrui Wu
MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery
  Classification
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. This paper proposes MIRepNet, the first EEG foundation model
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 demonstrated that MIRepNet consistently achieved
state-of-the-art performance, significantly outperforming both specialized and
generalized EEG models. Our code will be available on
GitHubhttps://github.com/staraink/MIRepNet.
MIRepNet, an EEG foundation model tailored for motor imagery, achieves state-of-the-art performance across multiple datasets using a hybrid pretraining strategy that combines self-supervised and supervised learning.
EEG foundation models motor imagery neurophysiologically-informed channel template self-supervised masked token reconstruction supervised MI classification rapid adaptation accurate decoding state-of-the-art performance