Brain-computer interface training for multimodal functional recovery in patients with brain injury: a case series

作者：Peili Cao, Shijie Guo, Gangli Zhang, Xiang Zan, Jun‐Hao Wang, Fulong Zhang, Javier Muñoz, Brandon Lucke-Wold, Rui Cheng · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2025 · DOI：10.21037/qims-2025-1136 · 被引用次数：8 · 研究领域：EEG and Brain-Computer Interfaces、Traumatic Brain Injury Research、Functional Brain Connectivity Studies

Background: Patients with impaired brain function often face sequelae such as limb movement, cognitive, and language impairment, and there are limitations in the efficiency of traditional rehabilitation methods. This study examined whether motor imagery-based brain-computer interface (BCI) training could promote multimodal functional recovery-including limb movement, speech, and cognition-in patients with subacute brain injury. Unlike traditional BCI research focused on single functional domains, we combined multidimensional clinical assessments with multimodal neural analysis to examine cross-network plasticity. Methods: Five patients with subacute brain injury (four males and one female; mean age 54.4±10.3 years) underwent 5 weeks of BCI training between 2021 and 2023. Pre- and post-intervention evaluations included the Fugl-Meyer Assessment Scale (FMA), Modified Ashworth Scale (MAS), Western Aphasia Battery (WAB), and Mini-Mental State Examination (MMSE). Neurophysiological metrics included classification accuracy (CA), power spectral density (PSD), and electroencephalography (EEG) topography. Functional connectivity analyses were conducted with functional magnetic resonance imaging (fMRI) and individualized connectomics based on the Human Connectome Project parcellation. Results: All five patients showed clinical improvement in motor, cognitive, or language functions. The average motor imagery CA increased by 14.2%. PSD flattening and event-related desynchronization (ERD)...