Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation

Abstract

The development of technologies for the treatment of movement disorders, like stroke, is still of particular interest in brain-computer interface (BCI) research. In this context, source localization methods (SLMs), that reconstruct the cerebral origin of brain activity measured outside the head, e.g., via electroencephalography (EEG), can add a valuable insight into the current state and progress of the treatment. However, in BCIs SLMs were often solely considered as advanced signal processing methods that are compared against other methods based on the classification performance alone. Though, this approach does not guarantee physiological meaningful results. We present an empirical comparison of three established distributed SLMs with the aim to use one for single-trial movement prediction. The SLMs wMNE, sLORETA, and dSPM were applied on data acquired from eight subjects performing voluntary arm movements. Besides the classification performance as quality measure, a distance metric was used to asses the physiological plausibility of the methods. For the distance metric, which is usually measured to the source position of maximum activity, we further propose a variant based on clusters that is better suited for the single-trial case in which several sources are likely and the actual maximum is unknown. The two metrics showed different results. The classification performance revealed no significant differences across subjects, indicating that all three methods are equally well-suited for single-trial movement prediction. On the other hand, we obtained significant differences in the distance measure, favoring wMNE even after correcting the distance with the number of reconstructed clusters. Further, distance results were inconsistent with the traditional method using the maximum, indicating that for wMNE the point of maximum source activity often did not coincide with the nearest activation cluster. In summary, the presented comparison might help users to select an appropriate SLM and to understand the implications of the selection. The proposed methodology pays attention to the particular properties of distributed SLMs and can serve as a framework for further comparisons.

Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

“…Another crucial methodological decision was choice of methods used to compare different algorithms. Previous studies have compared algorithms for source localization -identifying the origin of a small number of sources (Bai et al, 2007;Hassan et al, 2014;Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Seeland et al, 2018;Anzolin et al, 2019;Halder et al, 2019), such as known networks during task or simulated dipoles. These methods are not directly generalizable to resting-state data, where activity is not a point source but is distributed widely across the cortex.…”

“…Much effort has been made to assess the quality of source reconstruction algorithms in the literature, which mainly focuses on source localization, i.e. identifying the origin of a small number of sources, for example evoked potentials with ground truth based on known task-relevant activity (Bai et al, 2007;Hassan et al, 2014;Seeland et al, 2018;Halder et al, 2019), or simulated activity at a small number of dipoles (Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Anzolin et al, 2019;Halder et al, 2019).…”

A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high‐resolution atlas: Performance, precision, and parcellation

“…Here, we present a reduction of the HCP‐MMP atlas with 230 ROIs, based upon the forward transformation between source dynamics and MEG sensors. Since parcellation greatly reduces the data dimension, and previous studies of single‐trial/resting‐state MEG have predominantly focused on comparing algorithms at the voxel‐level (Hauk et al, 2011; Hauk et al, 2019; Hedrich et al, 2017; Little et al, 2018; Liu, Ganzetti, Wenderoth, & Mantini, 2018; Seeland et al, 2018), we further performed resolution and variance‐explained analyses to examine the extent to which voxel‐level comparisons between the six algorithms remain consistent when estimated source data is parcellated using our high‐resolution atlas.…”

Electroencephalography source localization analysis in epileptic children during a visual working‐memory task

“…Here, for our analysis, we used and compared three different 20 methods, namely the wMNE, the sLORETA and the dSPM that are described below.…”

“…Attal and Schwartz 19 compared the performance of three methods, namely the weighted minimum norm estimation (wMNE), sLORETA and the dynamic statistical parametric mapping (dSPM) for the characterization of distortions in cortical and subcortical regions using a realistic anatomical and electrophysiological model of deep brain activity. Seeland et al 20 compared wMNE, sLORETA and dSPM using EEG data taken from eight subjects performing voluntary arm movements.…”