Russo John S, Shiels Thomas A, Lin Chin-Hsuan Sophie, John Sam E, Grayden David B
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
Department of Medicine, Northern Health, Melbourne, Australia.
J Neural Eng. 2025 Mar 19;22(2). doi: 10.1088/1741-2552/adbec1.
There is limited work investigating brain-computer interface (BCI) technology in people with multiple sclerosis (pwMS), a neurodegenerative disorder of the central nervous system. Present work is limited to recordings at the scalp, which may be significantly altered by changes within the cortex due to volume conduction. The recordings obtained from the sensors, therefore, combine disease-related alterations and task-relevant neural signals, as well as signals from other regions of the brain that are not relevant. The current study aims to unmix signals affected by multiple sclerosis (MS) progression and BCI task-relevant signals using estimated source activity to improve classification accuracy.Data was collected from eight participants with a range of MS severity and ten neurotypical participants. This dataset was used to report the classification accuracy of imagined movements of the hands and feet at the sensor-level and the source-level in the current study.-means clustering of equivalent current dipoles was conducted to unmix temporally independent signals. The location of these dipoles was compared between MS and control groups and used for classification of imagined movement. Linear discriminant analysis classification was performed at each time-frequency point to highlight differences in frequency band delay.Source-level signal acquisition significantly improved decoding accuracy of imagined movement vs rest and movement vs movement classification in pwMS and controls. There was no significant difference found in alpha (7-13 Hz) and beta (13-30 Hz) band classification delay between the neurotypical control and MS group, including imagery of limbs with weakness or paralysis.This study is the first to demonstrate the advantages of source-level analysis for BCI applications in pwMS. The results highlight the potential for enhanced clinical outcomes and emphasize the need for longitudinal studies to assess the impact of MS progression on BCI performance, which is crucial for effective clinical translation of BCI technology.
针对中枢神经系统神经退行性疾病——多发性硬化症患者（pwMS）的脑机接口（BCI）技术的研究工作有限。目前的研究仅限于头皮记录，由于容积传导，皮质内的变化可能会显著改变这些记录。因此，从传感器获得的记录混合了与疾病相关的改变、与任务相关的神经信号以及来自大脑其他无关区域的信号。当前的研究旨在利用估计的源活动来分离受多发性硬化症（MS）进展影响的信号和与BCI任务相关的信号，以提高分类准确率。研究收集了八名患有不同MS严重程度的参与者和十名神经典型参与者的数据。在本研究中，该数据集用于报告在传感器层面和源层面上对手和脚想象运动的分类准确率。进行等效电流偶极子的均值聚类以分离时间上独立的信号。比较了MS组和对照组之间这些偶极子的位置，并将其用于想象运动的分类。在每个时频点进行线性判别分析分类，以突出频段延迟的差异。源层面的信号采集显著提高了pwMS患者和对照组中想象运动与静息以及运动与运动分类的解码准确率。在神经典型对照组和MS组之间，包括对有无力或麻痹肢体的想象，在α（7 - 13 Hz）和β（13 - 30 Hz）频段分类延迟方面未发现显著差异。本研究首次证明了源层面分析在pwMS患者BCI应用中的优势。结果突出了改善临床结果的潜力，并强调需要进行纵向研究以评估MS进展对BCI性能的影响，这对于BCI技术的有效临床转化至关重要。
