Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.e., inter-subject variability. Previous approaches try to mitigate this using task-specific (TS) EEG signals from the target subject in training. However, recording TS EEG signals requires time and limits its applicability in various fields. In contrast, resting state (RS) EEG signals are a viable alternative due to ease of acquisition with rich subject information. In this paper, we propose a novel subject-adaptive transfer learning strategy that utilizes RS EEG signals to adapt models on unseen subject data. Specifically, we disentangle extracted features into task- and subject-dependent features and use them to calibrate RS EEG signals for obtaining task information while preserving subject characteristics. The calibrated signals are then used to adapt the model to the target subject, enabling the model to simulate processing TS EEG signals of the target subject. The proposed method achieves state-of-the-art accuracy on three public benchmarks, demonstrating the effectiveness of our method in cross-subject EEG MI classification. Our findings highlight the potential of leveraging RS EEG signals to advance practical brain-computer interface systems. The code is available at https://github.com/SionAn/MICCAI2024-ResTL.

翻译：脑电图（EEG）运动想象（MI）分类是一项基础性任务，但由于个体间信号存在差异（即被试间变异性），该任务具有挑战性。以往的方法试图通过在训练中使用来自目标被试的任务特异性（TS）脑电信号来缓解这一问题。然而，记录TS脑电信号耗时较长，限制了其在各领域的适用性。相比之下，静息态（RS）脑电信号因其易于采集且富含被试信息，成为一种可行的替代方案。本文提出了一种新颖的自适应迁移学习策略，利用RS脑电信号使模型能够适应未见过的被试数据。具体而言，我们将提取的特征解耦为任务相关特征和被试相关特征，并利用它们对RS脑电信号进行校准，以在保留被试特征的同时获取任务信息。校准后的信号随后用于使模型适应目标被试，从而使模型能够模拟处理目标被试的TS脑电信号。所提方法在三个公开基准测试中达到了最先进的准确率，证明了该方法在跨被试脑电运动想象分类中的有效性。我们的研究结果凸显了利用RS脑电信号推动实用脑机接口系统发展的潜力。代码可在 https://github.com/SionAn/MICCAI2024-ResTL 获取。