脑电图源定位中的源方向检测 (AORI) 和时空 LCMV (ALCMV) 波束成形加速算法

Ava Yektaeian Vaziri, Bahador Makkiabadi
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引用次数: 0

摘要

本文阐述了针对脑电图(EEG)数据开发的两种高效源定位算法,旨在增强实时脑信号重建,同时解决传统方法在计算方面的难题。精确的脑电图信号源定位对于认知神经科学、神经康复和脑机接口(BCI)等应用至关重要。为了在精确信号源定位检测和改进信号重建方面取得重大进展,我们推出了加速线性约束最小方差(ALCMV)波束成形工具箱和加速脑信号源定位检测(AORI)工具箱。ALCMV 算法利用递归协方差矩阵计算加快了脑电图信号源重建速度,而 AORI 则将信号源方向检测从三维简化为一维,与传统方法相比减少了 66% 的计算负荷。利用模拟和真实脑电图数据,我们证明了这些算法保持了很高的精度,方向误差低于 0.2%,信号重建精度在 2% 以内。这些发现表明,所提出的工具箱大大提高了脑电图信号源定位的效率和速度,非常适合实际神经技术应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerated Algorithms for Source Orientation Detection (AORI) and Spatiotemporal LCMV (ALCMV) Beamforming in EEG Source Localization
This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing real-time brain signal reconstruction while addressing the computational challenges of traditional methods. Accurate EEG source localization is crucial for applications in cognitive neuroscience, neurorehabilitation, and brain-computer interfaces (BCIs). To make significant progress toward precise source orientation detection and improved signal reconstruction, we introduce the Accelerated Linear Constrained Minimum Variance (ALCMV) beamforming toolbox and the Accelerated Brain Source Orientation Detection (AORI) toolbox. The ALCMV algorithm speeds up EEG source reconstruction by utilizing recursive covariance matrix calculations, while AORI simplifies source orientation detection from three dimensions to one, reducing computational load by 66% compared to conventional methods. Using both simulated and real EEG data, we demonstrate that these algorithms maintain high accuracy, with orientation errors below 0.2% and signal reconstruction accuracy within 2%. These findings suggest that the proposed toolboxes represent a substantial advancement in the efficiency and speed of EEG source localization, making them well-suited for real-time neurotechnological applications.
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