J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
Sensors (Basel). 2023 Oct 1;23(19):8214. doi: 10.3390/s23198214.
The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: , , , , , and . We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0-100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.
本研究旨在测试一种新的方法（iCanClean），以去除在移动条件下记录的头皮 EEG 数据中的非脑源。我们创建了一个具有 10 个脑源、10 个污染源、头皮和头发的导电幻影头。我们测试了 iCanClean 在六种情况下去除伪影同时保留脑活动的能力：,,,, 和 。我们将 iCanClean 与另外三种方法进行了比较：Artifact Subspace Reconstruction（ASR）、Auto-CCA 和自适应滤波。在清洁前后，我们根据脑源和 EEG 通道之间的平均相关性计算了数据质量得分（0-100％）。无论存在哪种类型或数量的伪影，iCanClean 始终优于其他三种方法。最引人注目的结果是在所有伪影同时存在的情况下。从数据质量得分 15.7％（清洁前）开始， 条件下的得分提高到 55.9％，经过 iCanClean 处理后。相比之下，经过 ASR、Auto-CCA 和自适应滤波处理后，得分仅提高到 27.6％、27.2％和 32.9％。背景情况下，未经清洁时的得分是 57.2％（合理目标）。我们得出结论，iCanClean 提供了实时清除多个伪影源的能力，并可以促进 EEG 进行人类移动脑成像研究。
