基于脑电图的下肢变速步运动解码。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Attila Korik;Naomi Du Bois;Jose Sanchez Bornot;Niall McShane;Christoph Guger;Alessandra Del Felice;Olive Lennon;Damien Coyle
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引用次数: 0

摘要

从脑电图(EEG)中准确解码下肢运动对于开发脑机接口(BCI)控制的神经康复外骨骼至关重要。本研究采用两种方法(1)线性回归(LR)和(2)结合卷积神经网络(cnn)和长短期记忆(LSTM)单元的深度学习(DL)框架,研究了健康参与者(N=9)在地上行走过程中三个腓骨解剖标记处的3D速度解码。参与者被分为两组:G1组(n=5)在提示下向前走,自行后退;G2 (n=4)进行提示前进和后退。DL模型明显优于LR,在腓骨头的前后方向上获得了最高的解码精度(DA) (R = 0.63±0.06,M±SD)。地形分析发现,在8-40 Hz频段内,感觉运动皮层(加上G2的额叶区域)占主导地位。功能连通性(FC)分析显示有显著差异:只有G2显示有统计学意义的FC (p
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decoding the Variable Velocity of Lower-Limb Stepping Movements From EEG
Accurate decoding of lower-limb movement from electroencephalography (EEG) is essential for developing brain–computer interface (BCI) controlled exoskeletons in neurorehabilitation. This study investigates 3D velocity decoding at three fibular anatomical markers during overground stepping in healthy participants ( ${N}={9}$ ), using two approaches: (1) linear regression (LR) and (2) a deep learning (DL) framework combining convolutional neural networks (CNNs) and long short-term memory (LSTM) units. Participants were divided into two groups: G1 ( ${n}={5}$ ) performed cued forward and self-paced backward steps; G2 ( ${n}={4}$ ) performed cued forward and backward steps. The DL model significantly outperformed LR, achieving highest decoding accuracy (DA) in the forward-backward direction at the fibular head (R $= 0.63\pm 0.06$ , M±SD). Topographical analysis identified dominant contributions from the sensorimotor cortex (coupled with frontal regions in G2) within the 8–40 Hz band. Functional connectivity (FC) analysis revealed significant differences: only G2 showed statistically significant FC ( ${p}\lt {0.05}$ ), likely reflecting increased cognitive and sensorimotor demands under dual-cue conditions. In G2, FC occurred across delta (0–4 Hz), theta (4–8 Hz), alpha/mu (8–12 Hz), and low-beta (12–18 Hz) bands, linking motor areas associated with lower- and upper-limb control to other cortical regions, including the middle temporal gyrus (MTG), superior frontal gyrus (SFG), posterior cingulate cortex (PCC), superior parietal lobule (SPL), and supramarginal gyrus (SMG). These findings demonstrate that EEG-based 3D decoding of lower-limb kinematics is feasible during realistic locomotor tasks and suggest that cortical synchronization patterns vary with movement context. Our CNN-LSTM framework may support adaptive, intent-driven exoskeleton development for personalized neurorehabilitation.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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