用计算机视觉自动识别脑电图伪迹，提速7200倍且准确率达89.45%。
Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation
- 基于计算机视觉自动分类独立成分，替代人工判读。
- 处理速度提升7200倍，准确率89.45%，可支持近实时分析。
- 兼容ICLabel和EEGLab，适合大规模脑电研究与临床应用。
脑电图（EEG）是研究脑部疾病和行为变化的重要非侵入性工具，但其在认知发展研究中面临时间分辨率、信号源定位及伪迹干扰等挑战。独立成分分析（ICA）能有效分离头皮电极记录信号中的源生成过程，但传统方法需人工逐个检查、选择和解释独立成分（ICs），耗时且依赖经验。本研究提出一种基于计算机视觉的自动化ICA伪迹识别标注工具，兼容ICLabel和EEGLab等主流软件。该系统将人工处理流程自动化，使处理效率提升7200倍，同时达到89.45%的分类准确率，显著加速大规模脑电研究，并支持医疗场景下的近实时脑活动剔除任务。
原文摘要 · Abstract (English)
The electroencephalogram (EEG) is a valuable and widely applied tool for investigating brain disorders and behavioral changes. It offers a minimally restrictive and non-invasive method. However, challenges in using EEG for cognitive development studies include temporal resolution, signal source localization, and EEG artifacts. Careful consideration of these factors is essential for informed application of EEG technology. Independent component analysis (ICA) effectively isolates source generator processes from signals recorded by multiple, adjacent EEG scalp electrodes. Although ICA decomposition requires manual inspection, selection, and interpretation of independent components (ICs), this process is time consuming and demands expertise. Automated IC classification can achieve sufficient accuracy, expediting large scale EEG research and enabling near real time applications in conjunction with brain activity rejection tasks, which are crucial for medical specialists. This study introduces an automated computer vision based ICA rejection labeling tool compatible with widely used software interfaces like ICLabel and EEGLab. By automating the manual task, the proposed system reduces processing time by 7200 fold and achieves an accuracy of 89.45%.
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