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 Jan 23;22(1). doi: 10.1088/1741-2552/adaa1d.
Multiple sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A brain-computer interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS has been confined to exploring the P300 response and brain signals associated with attempted movement. The current study aims to expand the MS-BCI literature by highlighting the feasibility of decoding MS imagined movement.We collected electroencephalography data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement vs. rest and vs. movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared.In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency.This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance.
多发性硬化症（MS）是一种异质性自身免疫介导的疾病，会影响中枢神经系统，通常表现为疲劳和进行性肢体损伤。由于上肢和下肢的无力或瘫痪，这会对生活质量产生重大影响。脑机接口（BCI）旨在通过控制外部设备（如轮椅）来恢复生活质量。然而，针对MS患者的BCI研究有限，仅限于探索P300反应以及与尝试运动相关的脑信号。当前的研究旨在通过强调解码MS想象运动的可行性来扩展MS-BCI的文献。我们收集了八名有各种MS症状的参与者和十名神经典型对照参与者的脑电图数据。参与者按照“是/否”协议的指示进行手部和脚部的想象运动。使用二元正则化线性判别分析在各个时频点对想象运动与休息以及与运动进行分类。比较了提供最大准确率的频段以及相关的潜伏期。在所有MS参与者中，分类算法在至少一项想象运动与休息的分类以及大多数运动与运动的分类中实现了70%以上的准确率。有无力或瘫痪的肢体与神经典型对照的分类之间没有显著差异。MS组和对照组在相似的潜伏期内，在α（7-13Hz）和β（16-30Hz）频段内都具有可解码信息。本研究首次证明了解码MS患者想象运动的可行性。作为P300反应的替代方法，基于运动想象的BCI控制也可以与现有的运动想象疗法相结合，以补充MS康复。这些有前景的结果值得进一步进行长期的BCI研究，以调查MS进展对分类性能的影响。
