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Member, IEEE（IEEE成员）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对EEG在认知发展研究中的挑战，引入基于计算机视觉的自动化ICA拒绝标记工具，兼容常用软件接口，通过自动化人工任务，大幅减少处理时间，提高准确率，加速大规模EEG研究及脑电活动抑制任务应用。
AI 中文摘要
脑电图（EEG）是研究脑部疾病和行为变化的重要且广泛应用的工具，具有微创、非侵入性的特点。然而，在认知发展研究中使用EEG存在时间分辨率、信号源定位和EEG伪影等挑战。独立成分分析（ICA）能有效分离源生成过程，但ICA分解需人工检查、选择和解释独立成分，耗时且需专业知识。自动化IC分类可提高准确性，加速大规模EEG研究。本研究介绍了一种基于计算机视觉的自动化ICA拒绝标记工具，与ICLabel和EEGLab等软件接口兼容，将处理时间减少7200倍，准确率达89.45%。
英文摘要
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%.
Comments7 pages, 10 figures, Conference
Journal refICMLA 2024
DOI：10.1109/ICMLA61862.2024.00229 10.1109/ICMLA61862.2024.00229 10.1109/ICMLA61862.2024
