Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio which makes extraction of meaningful neural information challenging. Artifact Subspace Reconstruction (ASR) is one of the most widely used artifact filtering techniques in EEG-based BCI applications, owing to its real-time applicability. ASR reconstructs artifact-free signals by operating in Principal Component (PC) space within sliding windows. However, ASR performance is critically sensitive to its threshold parameter - an incorrect threshold risks removing task-relevant neural features alongside artifacts. Furthermore, since PCs are linear combinations of all channels, subspace reconstruction in PC space may alter the underlying data structure, potentially discarding essential neural information. To address these limitations, we propose nASR, a novel end-to-end trainable Keras layer that jointly optimizes artifact rejection and downstream decoding. nASR introduces two trainable threshold parameters: K, which governs artifact detection in PC variance space, and L, which quantifies eigen-spread to pinpoint the primary artifact--contributing channels, enabling selective channel-level reconstruction that preserves clean channel information. An ablation study comprising five model variants (m01 - m05), evaluated across two subjects from the BCI Competition IV Dataset 1, confirms that nASR variants consistently outperform traditional ASR on test classification metrics, while achieving a 6-8x reduction in inference time, making nASR a strong candidate for real-time BCI applications demanding both low latency and high decoding performance.

翻译：脑电图信号极易受到伪迹干扰，导致信噪比低下，从而难以提取有意义的神经信息。伪迹子空间重建（ASR）因其具备实时应用能力，成为基于脑电图的脑机接口应用中最广泛使用的伪迹滤波技术之一。ASR通过在滑动窗口内的主成分空间中运行来重建无伪迹信号。然而，ASR的性能对其阈值参数极为敏感——不恰当的阈值可能会在去除伪迹的同时移除与任务相关的神经特征。此外，由于主成分是所有通道的线性组合，因此主成分空间中的子空间重建可能会改变底层数据结构，从而可能丢弃必要的神经信息。为解决这些局限性，我们提出了nASR——一种新颖的、可端到端训练的Keras层，它联合优化了伪迹抑制与下游解码。nASR引入了两个可训练的阈值参数：K用于控制主成分方差空间中的伪迹检测，L用于量化特征值分散度以定位主要的伪迹贡献通道，从而实现选择性通道级重建，保留干净通道的信息。一项包含五种模型变体（m01至m05）的消融研究，在来自BCI Competition IV数据集1的两名受试者上进行了评估，证实nASR变体在测试分类指标上始终优于传统ASR，同时实现了6-8倍的推理时间缩减，这使得nASR成为需要低延迟和高解码性能的实时脑机接口应用的有力候选方案。