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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Brain Imaging Methods
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1505017

Accelerated Algorithms for Source Orientation Detection and Spatiotemporal LCMV Beamforming in EEG Source Localization

Provisionally accepted
Ava Yektaeian Vaziri Ava Yektaeian Vaziri *Bahador Makki Abadi Bahador Makki Abadi
  • Tehran University of Medical Sciences, Tehran, Iran

The final, formatted version of the article will be published soon.

    This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing realtime 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.

    Keywords: EEG source localization 1, beamforming, neural signal processing, lcmv, Accelerated algorithms, Source orientation detection, Recursive calculations, brain-computer interface (BCI)

    Received: 01 Oct 2024; Accepted: 16 Dec 2024.

    Copyright: © 2024 Yektaeian Vaziri and Makki Abadi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ava Yektaeian Vaziri, Tehran University of Medical Sciences, Tehran, Iran

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.