Electroencephalography (EEG) based Brain Computer Interface (BCI) systems play a significant role in facilitating how individuals with neurological impairments effectively interact with their environment. In real world applications of BCI system for clinical assistance and rehabilitation training, the EEG classifier often needs to learn on sequentially arriving subjects in an online manner. As patterns of EEG signals can be significantly different for different subjects, the EEG classifier can easily erase knowledge of learnt subjects after learning on later ones as it performs decoding in online streaming scenario, namely catastrophic forgetting. In this work, we tackle this problem with a memory-based approach, which considers the following conditions: (1) subjects arrive sequentially in an online manner, with no large scale dataset available for joint training beforehand, (2) data volume from the different subjects could be imbalanced, (3) decoding difficulty of the sequential streaming signal vary, (4) continual classification for a long time is required. This online sequential EEG decoding problem is more challenging than classic cross subject EEG decoding as there is no large-scale training data from the different subjects available beforehand. The proposed model keeps a small balanced memory buffer during sequential learning, with memory data dynamically selected based on joint consideration of data volume and informativeness. Furthermore, for the more general scenarios where subject identity is unknown to the EEG decoder, aka. subject agnostic scenario, we propose a kernel based subject shift detection method that identifies underlying subject changes on the fly in a computationally efficient manner. We develop challenging benchmarks of streaming EEG data from sequentially arriving subjects with both balanced and imbalanced data volumes, and performed extensive experiments with a detailed ablation study on the proposed model. The results show the effectiveness of our proposed approach, enabling the decoder to maintain performance on all previously seen subjects over a long period of sequential decoding. The model demonstrates the potential for real-world applications.

中文翻译：

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使用平衡且信息丰富的内存缓冲区对流式脑电图信号进行在线连续解码

基于脑电图 (EEG) 的脑机接口 (BCI) 系统在促进神经损伤患者如何有效地与环境互动方面发挥着重要作用。在脑机接口系统用于临床辅助和康复训练的现实应用中，脑电图分类器通常需要以在线方式学习顺序到达的受试者。由于不同受试者的脑电图信号模式可能存在显着差异，因此脑电图分类器在在线流媒体场景中进行解码时，在学习了后面的科目后很容易擦除所学科目的知识，即灾难性遗忘。在这项工作中，我们采用基于记忆的方法来解决这个问题，该方法考虑以下条件：（1）受试者以在线方式顺序到达，事先没有可用于联合训练的大规模数据集，（2）来自不同的对象可能会不平衡，（3）顺序流信号的解码难度不同，（4）需要长时间连续分类。这种在线顺序脑电图解码问题比经典的跨主题脑电图解码更具挑战性，因为事先没有来自不同主题的大规模训练数据。该模型在顺序学习过程中保持较小的平衡内存缓冲区，并根据数据量和信息量动态选择内存数据。此外，对于更一般的场景，即脑电图解码器未知受试者身份。在与主题无关的场景中，我们提出了一种基于内核的主题转移检测方法，该方法以计算有效的方式动态识别潜在的主题变化。我们开发了具有挑战性的基准，从顺序到达的受试者中获取具有平衡和不平衡数据量的流式脑电图数据，并对所提出的模型进行了详细的消融研究进行了广泛的实验。结果显示了我们提出的方法的有效性，使解码器能够在长时间的顺序解码中保持所有先前看到的主题的性能。该模型展示了实际应用的潜力。