Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), Via Previati 1/E, 23900 Lecco, Italy.
Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), via Fratelli Cervi 93, 20054 Segrate, Italy.
Sensors (Basel). 2021 Oct 22;21(21):7014. doi: 10.3390/s21217014.
Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.
脑电图(EEG)和肌电图(EMG)是广泛应用且广为人知的定量技术,分别用于在皮质和肌肉水平采集生物信号。实际上,它们为不同领域(如生理和认知)和研究领域(包括神经运动康复)提供了相关的知识。到目前为止,EEG 和 EMG 技术已经被独立地用于指导或评估康复的结果,根据研究的目的,优先选择一种技术而不是另一种技术。最近,EEG 和 EMG 的结合开始被认为是提高康复效果的潜在突破方法。然而,由于这是一个相对较新的研究领域,我们观察到,尚未确定综合审查,也没有用于同时采集和处理的标准程序和设置。因此,本文对 EEG 和 EMG 的应用进行了系统评价,旨在评估和评估神经运动表现,重点关注康复领域的皮质-肌肉相互作用。从科学数据库中总共确定了 213 篇文章,经过严格审查,本文详细分析了其中的 55 篇。大多数应用都集中在对中风患者的研究上,康复的目标通常是上肢或下肢。关于用于采集和处理数据的方法学方法,我们的结果表明,在该领域中,同时采集 EEG 和 EMG 是相当常见的,但它主要是作为更具体的 EEG 方法的支持技术来使用。非特定的处理方法,如 EEG-EMG 相干性,用于提供组合的 EEG/EMG 信号分析,但很少同时使用两种信号,以分析每个领域的黄金标准技术。未来的方向可能是面向多领域的方法,能够充分利用 EEG 和 EMG 的组合潜力,例如针对更广泛的病理,并实施更结构化的临床试验,以确认当前试点研究的结果。