# NIRS-EMG for Clinical Applications: A Systematic Review  
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1 Istituto di Sistemi e Tecnologie Industriial Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), [PERSON] 12, 20133 Milan, Italy 1  
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2 Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy 2  
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2 Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy 2  
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3 Istituto di Fotonica e Nanotecnologie (IFN), Consiglio Nazionale delle Ricerche (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy 3  
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A multi-parameter assessment of patients may investigate several levels of the hierarchical organization of the neuro-musculo-skeletal system, assessing correlations within the neural, the muscular, and the kinematic activity. Such results can be achieved by coupling techniques such as EEG and EMG (as well as with kinematics tracking) or MEG and EMG, in order to investigate neural processes and their motor correlates [7]. The advantage to adding NIRS into this framework is to assess neurovascular coupling during exercises [8]. However, techniques can also be combined to assess the same domain under different perspectives. In a similar manner to the analysis of brain tissues and activity when coupling EEG and NIRS, which has been recently discussed [9], in this review, we focus on EMG and NIRS techniques which, when combined together, can simultaneously assess the electrical and hemodynamic activity of the working muscle, providing a strong characterization of muscles under multi-variate perspectives, which cannot be provided with the other mentioned techniques. At the same time, as far as muscle evaluations are needed, these two techniques allow the investigation of muscle activity in detail and with relatively rapid monitoring procedures which are compliant with clinical treatments and investigations. A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of innervated muscle fibers and it is used as a source of assessment of neural activation in muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging [10]. Several time domain, frequency domain, and time-frequency methods for analyzing EMG, especially related to rehabilitative purposes, have been evaluated in recent works [11; 12]. Thus, algorithms have been developed in order to automatically evaluate aberrant patterns in muscle activity [13], or to provide an in-depth characterization of motor-unit alterations in post-stroke patients [14]. A comprehensive literature of EMG applications to clinical purposes can be found, spanning through a variety of investigated muscles and pathologies, in order to detect abnormal patterns with evaluation, diagnosis, and rehabilitation aims. We report on a subset of paradigmatic applications that include, but are not limited to, the assessment of lower-back pain [15; 16]; the investigation of patterns for motor recruitment [17]; the analysis of walking patterns in Parkinson's [18]; gait pattern abnormality detection in multiple sclerosis [19]; applications to stroke hand rehabilitation [20]; and many others. On the other hand, NIRS techniques are based on the use of light to monitor, in a non-invasive way, the hemodynamics of a muscle, with the aim of extrapolating information about the mechanism of local muscle oxidative metabolism during exercises. In the clinical setting, NIRS allows clinicians to follow-up the impairments over time, to identify possible correlations with other symptoms, and to evaluate the effects of particular rehabilitative interventions [5]. In fact, through the estimation of the oxy (O\({}_{2}\)Hb)- and deoxy (HHb)-hemoglobin content, or through the derived parameters of total hemoglobin content (tHb) and oxygen saturation (SO\({}_{2}\)), it is possible to understand the dynamic balance between oxygen delivery and oxygen consumption in muscle [21].