Human Neuromechanics Laboratory, School of Kinesiology, University of Michigan, Ann Arbor, MI, USA.
J Neuroeng Rehabil. 2012 Jun 9;9:35. doi: 10.1186/1743-0003-9-35.
Electroencephalography (EEG) combined with independent component analysis enables functional neuroimaging in dynamic environments including during human locomotion. This type of functional neuroimaging could be a powerful tool for neurological rehabilitation. It could enable clinicians to monitor changes in motor control related cortical dynamics associated with a therapeutic intervention, and it could facilitate noninvasive electrocortical control of devices for assisting limb movement to stimulate activity dependent plasticity. Understanding the relationship between electrocortical dynamics and muscle activity will be helpful for incorporating EEG-based functional neuroimaging into clinical practice. The goal of this study was to use independent component analysis of high-density EEG to test whether we could relate electrocortical dynamics to lower limb muscle activation in a constrained motor task. A secondary goal was to assess the trial-by-trial consistency of the electrocortical dynamics by decoding the type of muscle action.
We recorded 264-channel EEG while 8 neurologically intact subjects performed isometric and isotonic, knee and ankle exercises at two different effort levels. Adaptive mixture independent component analysis (AMICA) parsed EEG into models of underlying source signals. We generated spectrograms for all electrocortical source signals and used a naïve Bayesian classifier to decode exercise type from trial-by-trial time-frequency data.
AMICA captured different electrocortical source distributions for ankle and knee tasks. The fit of single-trial EEG to these models distinguished knee from ankle tasks with 80% accuracy. Electrocortical spectral modulations in the supplementary motor area were significantly different for isometric and isotonic tasks (p < 0.05). Isometric contractions elicited an event related desynchronization (ERD) in the α-band (8-12 Hz) and β-band (12-30 Hz) at joint torque onset and offset. Isotonic contractions elicited a sustained α- and β-band ERD throughout the trial. Classifiers based on supplementary motor area sources achieved a 4-way classification accuracy of 69% while classifiers based on electrocortical sources in multiple brain regions achieved a 4-way classification accuracy of 87%.
Independent component analysis of EEG reveals unique spatial and spectro-temporal electrocortical properties for different lower limb motor tasks. Using a broad distribution of electrocortical signals may improve classification of human lower limb movements from single-trial EEG.
脑电图（EEG）与独立成分分析相结合，可以在包括人类运动在内的动态环境中进行功能神经影像学研究。这种类型的功能神经影像学可能是神经康复的有力工具。它可以使临床医生监测与治疗干预相关的运动控制相关皮质动力学的变化，并促进用于辅助肢体运动的非侵入性电皮质控制，以刺激活动依赖性可塑性。了解皮质电活动与肌肉活动之间的关系将有助于将基于脑电图的功能神经影像学纳入临床实践。本研究的目的是使用高密度 EEG 的独立成分分析来测试我们是否可以将皮质电活动与受约束运动任务中的下肢肌肉激活相关联。次要目标是通过解码肌肉动作的类型来评估皮质电活动的逐次试验一致性。
我们记录了 264 通道 EEG，同时 8 名神经完整的受试者在两种不同的努力水平下进行等长和等张的膝关节和踝关节运动。自适应混合独立成分分析（AMICA）将 EEG 解析为潜在源信号的模型。我们为所有皮质电源信号生成了频谱图，并使用朴素贝叶斯分类器根据逐次试验的时频数据解码运动类型。
AMICA 捕获了踝关节和膝关节任务的不同皮质电源分布。单次试验 EEG 对这些模型的拟合可以以 80%的准确率区分膝关节和踝关节任务。等长和等张任务的运动辅助区皮质电谱调制有显著差异（p < 0.05）。等长收缩在关节转矩起始和结束时诱发了 α 波段（8-12 Hz）和 β 波段（12-30 Hz）的事件相关去同步化（ERD）。等张收缩在整个试验中诱发持续的 α 和 β 波段 ERD。基于运动辅助区源的分类器达到了 69%的 4 路分类准确率，而基于多个脑区皮质电源的分类器达到了 87%的 4 路分类准确率。
EEG 的独立成分分析揭示了不同下肢运动任务的独特空间和时频谱皮质电特性。使用广泛分布的皮质电信号可能会提高从单次试验 EEG 中分类人类下肢运动的能力。
