基于ICA和SSLOFO的P300时空分析

Bo Hong, Hesheng Liu, P. Schimpf, Shangkai Gao, N. Thakor
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引用次数: 0

摘要

利用逆方法揭示脑电源活动的空间信息有助于脑机接口系统的改进。本文提出了一种将独立分量分析(ICA)和一种新开发的逆算法SSLOFO相结合的方法来鲁棒重建P300皮质源。首先利用时空优化过程提取目标独立分量,然后利用slofo对目标分量源进行定位。初步研究表明,我们的方法能够基于5次平均脑电图定位P300的来源,结果与fMRl等其他功能成像研究的结果一致。一项研究也证明了我们的方法的鲁棒性,该研究表明P300源在左右TPJ区域周围稳定地重建
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatio-temporal analysis of P300 using ICA and SSLOFO
Spatial information of EEG source activity revealed by inverse methods may contribute to an improvement of the BCI systems. This paper proposes an approach that integrates the independent component analysis (ICA) and a newly developed inverse algorithm termed SSLOFO to robustly reconstruct cortical sources of P300. The target independent components are first extracted using a spatio-temporal optimization process and then SSLOFO is employed to localize the sources of the target components. Preliminary studies demonstrate our method is able to localize sources of P300 based on 5-trial-averaged EEG and the results are consistent with the findings of other functional imaging studies such as fMRl. The robustness of our approach is also proved by a study which indicates the P300 sources are stably reconstructed around the left and right TPJ areas
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