Yektaeian Vaziri Ava, Makkiabadi Bahador
Department of Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.
Research Center for Biomedical Technologies and Robotics (RCBTR), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Science (TUMS), Tehran, Iran.
Front Neurosci. 2025 Mar 4;18:1505017. doi: 10.3389/fnins.2024.1505017. eCollection 2024.
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.
本文阐述了两种用于脑电图（EEG）数据的高效源定位算法的开发，旨在增强实时脑信号重建，同时应对传统方法的计算挑战。准确的EEG源定位对于认知神经科学、神经康复和脑机接口（BCI）的应用至关重要。为了在精确的源方向检测和改进的信号重建方面取得重大进展，我们引入了加速线性约束最小方差（ALCMV）波束形成工具箱和加速脑源方向检测（AORI）工具箱。ALCMV算法通过利用递归协方差矩阵计算来加速EEG源重建，而AORI将源方向检测从三维简化为一维，与传统方法相比，计算量减少了66%。使用模拟和真实EEG数据，我们证明这些算法保持了高精度，方向误差低于0.2%，信号重建精度在2%以内。这些发现表明，所提出的工具箱在EEG源定位的效率和速度方面取得了重大进展，使其非常适合实时神经技术应用。
