标题： 基于自动眨眼的BCI应用中不良脑电通道检测 显示英文标题

标题： Automatic Blink-based Bad EEG channels Detection for BCI Applications

摘要： 在脑机接口（BCI）应用中，噪声是一个持续的挑战，常常影响对准确数据解释至关重要的脑电图（EEG）信号的质量。 本文专注于优化信噪比（SNR）以提高BCI性能，其中通道选择是实现这一增强的关键方法。 使用Eye-BCI多模态数据集来解决由非生物伪影引起的故障脑电通道检测和消除问题，例如故障电极和电源线干扰。 本研究的核心是通过自适应眨眼校正和去漂移（ABCD）算法自动检测有问题的通道。 该方法利用眨眼传播模式来识别受伪影或故障影响的通道。 此外，分段SNR拓扑图和源定位图被用来说明通过比较左、右手抓握运动想象（MI）来移除通道的影响。 分类准确率进一步支持了ABCD算法的价值，在31名受试者（63次会话）中达到平均分类准确率为93.81% [74.81%; 98.76%]（置信度为95%），显著超过了传统方法如独立成分分析（ICA）（79.29% [57.41%; 92.89%]）和伪影子空间重建（ASR）（84.05% [62.88%; 95.31%]）。 这些结果突显了通道选择的关键作用以及利用眨眼模式检测不良脑电通道的潜力，为通过减少噪声和提高信号质量来改进实时或离线BCI系统提供了有价值的见解。 显示英文摘要

摘要： In Brain-Computer Interface (BCI) applications, noise presents a persistent challenge, often compromising the quality of EEG signals essential for accurate data interpretation. This paper focuses on optimizing the signal-to-noise ratio (SNR) to improve BCI performance, with channel selection being a key method for achieving this enhancement. The Eye-BCI multimodal dataset is used to address the issue of detecting and eliminating faulty EEG channels caused by non-biological artifacts, such as malfunctioning electrodes and power line interference. The core of this research is the automatic detection of problematic channels through the Adaptive Blink-Correction and De-Drifting (ABCD) algorithm. This method utilizes blink propagation patterns to identify channels affected by artifacts or malfunctions. Additionally, segmented SNR topographies and source localization plots are employed to illustrate the impact of channel removal by comparing Left and Right hand grasp Motor Imagery (MI). Classification accuracy further supports the value of the ABCD algorithm, reaching an average classification accuracy of 93.81% [74.81%; 98.76%] (confidence interval at 95% confidence level) across 31 subjects (63 sessions), significantly surpassing traditional methods such as Independent Component Analysis (ICA) (79.29% [57.41%; 92.89%]) and Artifact Subspace Reconstruction (ASR) (84.05% [62.88%; 95.31%]). These results underscore the critical role of channel selection and the potential of using blink patterns for detecting bad EEG channels, offering valuable insights for improving real-time or offline BCI systems by reducing noise and enhancing signal quality.