MixNet：融合经典与现代方法以构建运动想象EEG分类综合流水线
MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification
AI总结：
提出MixNet框架，融合FBCSP频谱-空间特征与MIN2Net多任务学习，并通过自适应梯度混合平衡任务训练，在六个基准及低密度EEG场景中取得最优分类性能。
摘要：
深度学习（DL）的最新进展显著影响了基于运动想象（MI）的脑机接口（BCI）系统，增强了脑电图（EEG）信号的解码能力。然而，大多数研究难以在MI任务中识别跨受试者的判别性模式，从而限制了MI分类性能。本文提出MixNet，这是一种新颖的分类框架，旨在通过利用MI数据中的频谱-空间信号以及名为MIN2Net的多任务学习架构进行分类来克服这一局限。其中，频谱-空间信号通过在MI数据上使用滤波器组共空间模式（FBCSP）方法生成。由于多任务学习架构用于分类任务，各任务中的学习可能表现出不同的泛化速率以及跨任务的潜在过拟合。为解决该问题，我们实施自适应梯度混合，同时调节多个损失权重，并根据每个任务的泛化/过拟合倾向调整其学习节奏。在六个不同数据规模的基准数据集上的实验结果表明，MixNet在受试者依赖和受试者独立设置下始终优于所有最先进算法。最后，低密度EEG MI分类结果表明，MixNet优于所有最先进算法，为物联网（IoT）应用提供了有前景的意义，例如基于低密度导联布局的轻量级便携式EEG可穿戴设备。
英文摘要：
Recent advances in deep learning (DL) have significantly impacted motor imagery (MI)-based brain-computer interface (BCI) systems, enhancing the decoding of electroencephalography (EEG) signals. However, most studies struggle to identify discriminative patterns across subjects during MI tasks, limiting MI classification performance. In this article, we propose MixNet, a novel classification framework designed to overcome this limitation by utilizing spectral-spatial signals from MI data, along with a multitask learning architecture named MIN2Net, for classification. Here, the spectral-spatial signals are generated using the filter-bank common spatial patterns (FBCSPs) method on MI data. Since the multitask learning architecture is used for the classification task, the learning in each task may exhibit different generalization rates and potential overfitting across tasks. To address this issue, we implement adaptive gradient blending, simultaneously regulating multiple loss weights and adjusting the learning pace for each task based on its generalization/overfitting tendencies. Experimental results on six benchmark data sets of different data sizes demonstrate that MixNet consistently outperforms all state-of-the-art algorithms in subject-dependent and -independent settings. Finally, the low-density EEG MI classification results show that MixNet outperforms all state-of-the-art algorithms, offering promising implications for Internet of Thing (IoT) applications, such as lightweight and portable EEG wearable devices based on low-density montages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
