Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Department of Electrical Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Med Biol Eng Comput. 2024 Oct;62(10):3073-3088. doi: 10.1007/s11517-024-03125-9. Epub 2024 May 21.
One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on and norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.
神经影像学中最重要的需求之一是具有高时空分辨率的大脑动态源成像。EEG 源成像从 EEG 记录中估计潜在的源,提供了内在的高时间分辨率的增强空间分辨率。为了确保在欠定源重建问题中的可识别性,对 EEG 源的约束是必不可少的。本文介绍了一种基于时空约束和动态源成像算法的源活动估计新方法。该方法通过将神经活动的时间演化纳入正则化函数来提高时间分辨率。此外,基于 和 范数的两个空间正则化约束在变换域中应用,以解决焦点和扩散神经活动,通过空间梯度和拉普拉斯变换实现。使用合成数据集进行的定量性能评估讨论了源范围、源数量、相关水平和 SNR 水平等参数对时空度量的影响。结果表明,与包括 STRAPS、sLORETA、SBL、dSPM 和 MxNE 在内的最先进的逆解方法相比,所提出的方法提供了优越的空间和时间重建。这种改进归因于变换后的时空约束的同时集成。当应用于真实的听觉 ERP 数据集时,我们的算法准确地重建了大脑源时间序列和位置,有效地识别了听觉诱发电位的起源。总之,我们提出的具有时空约束的方法在估计源分布和时间过程方面优于最先进的算法。