Corticomuscular interactions during different movement periods in a multi-joint compound movement

While much is known about motor control during simple movements, corticomuscular communication profiles during compound movement control remain largely unexplored. Here, we aimed at examining frequency band related interactions between brain and muscles during different movement periods of a bipedal squat (BpS) task utilizing regression corticomuscular coherence (rCMC), as well as partial directed coherence (PDC) analyses. Participants performed 40 squats, divided into three successive movement periods (Eccentric (ECC), Isometric (ISO) and Concentric (CON)) in a standardized manner. EEG was recorded from 32 channels specifically-tailored to cover bilateral sensorimotor areas while bilateral EMG was recorded from four main muscles of BpS. We found both significant CMC and PDC (in beta and gamma bands) during BpS execution, where CMC was significantly elevated during ECC and CON when compared to ISO. Further, the dominant direction of information flow (DIF) was most prominent in EEG-EMG direction for CON and EMG-EEG direction for ECC. Collectively, we provide novel evidence that motor control during BpS is potentially achieved through central motor commands driven by a combination of directed inputs spanning across multiple frequency bands. These results serve as an important step toward a better understanding of brain-muscle relationships during multi joint compound movements.V.V.N was supported by the HSE Basic Research Program and the Russian Academic Excellence Project '5–100'. This study was supported by the Max-Planck Society

An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation

Background Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New Method In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results The classification accuracy of the CBPT-based BCI system was found to be 92.81±2.09% for the healthy experimental group and 84.53±4.58% for the patients’ group. Comparison with existing method The CBPT method significantly (p−value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms

Neuroimaging of human motor control in real world scenarios: from lab to urban environment

The main goal of this research programme was to explore the neurophysiological correlates of human motor control in real-world scenarios and define mechanism-specific markers that could eventually be employed as targets of novel neurorehabilitation practice. As a result of recent developments in mobile technologies it is now possible to observe subjects' behaviour and monitor neurophysiological activity whilst they perform natural activities freely. Investigations in real-world scenarios would shed new light on mechanisms of human motor control previously not observed in laboratory settings and how they could be exploited to improve rehabilitative interventions for the neurologically impaired. This research programme was focussed on identifying cortical mechanisms involved in both upper- (i.e. reaching) and lower-limb (i.e. locomotion) motor control. Complementary results were obtained by the simultaneous recordings of kinematic, electromyographic and electrocorticographic signals. To study motor control of the upper-limb, a lab­based setup was developed, and the reaching movement of healthy young individuals was observed in both stable and unstable (i.e. external perturbation) situations. Robot-mediated force-field adaptation has the potential to be employed in rehabilitation practice to promote new skills learning and motor recovery. The muscular (i.e. intermuscular couplings) and neural (i.e. spontaneous oscillations and cortico­muscular couplings) indicators of the undergoing adaptation process were all symbolic of adaptive strategies employed during early stages of adaptation. The medial frontal, premotor and supplementary motor regions appeared to be the principal cortical regions promoting adaptive control and force modulation. To study locomotion control, a mobile setup was developed and daily life human activities (i.e. walking while conversing, walking while texting with a smartphone) were investigated outside the lab. Walking in hazardous environments or when simultaneously performing a secondary task has been demonstrated to be challenging for the neurologically impaired. Healthy young adults showed a reduced motor performance when walking in multitasking conditions, during which whole-brain and task-specific neural correlates were observed. Interestingly, the activity of the left posterior parietal cortex was predictive of the level of gait stability across individuals, suggesting a crucial role of this area in gait control and determination of subject specific motor capabilities. In summary, this research programme provided evidence on different cortical mechanisms operative during two specific scenarios for "real­world" motor behaviour in and outside the laboratory-setting in healthy subjects. The results suggested that identification of neuro-muscular indicators of specific motor control mechanisms could be exploited in future "real-world" rehabilitative practice

Influencing factors of corticomuscular coherence in stroke patients

Stroke, also known as cerebrovascular accident, is an acute cerebrovascular disease with a high incidence, disability rate, and mortality. It can disrupt the interaction between the cerebral cortex and external muscles. Corticomuscular coherence (CMC) is a common and useful method for studying how the cerebral cortex controls muscle activity. CMC can expose functional connections between the cortex and muscle, reflecting the information flow in the motor system. Afferent feedback related to CMC can reveal these functional connections. This paper aims to investigate the factors influencing CMC in stroke patients and provide a comprehensive summary and analysis of the current research in this area. This paper begins by discussing the impact of stroke and the significance of CMC in stroke patients. It then proceeds to elaborate on the mechanism of CMC and its defining formula. Next, the impacts of various factors on CMC in stroke patients were discussed individually. Lastly, this paper addresses current challenges and future prospects for CMC

Development of EEG-based technologies for the characterization and treatment of neurological diseases affecting the motor function

This thesis presents a set of studies applying signal processing and data mining techniques in real-time working systems to register, characterize and condition the movement-related cortical activity of healthy subjects and of patients with neurological disorders affecting the motor function. Patients with two of the most widespread neurological affections impairing the motor function are considered here: patients with essential tremor and patients who have suffered a cerebro-vascular accident. The different chapters in the presented thesis show results regarding the normal cortical activity associated with the planning and execution of motor actions with the upper-limb, and the pathological activity related to the patients' motor dysfunction (measurable with muscle electrodes or movement sensors). The initial chapters of the book present i) a revision of the basic concepts regarding the role of the cerebral cortex in the motor control and the way in which the electroencephalographic activity allows its analysis and conditioning, ii) a study on the cortico-muscular interaction at the tremor frequency in patients with essential tremor under the effects of a drug reducing their tremor, and finally iii) a study based on evolutionary algorithms that aims to identify cortical patterns related to the planning of a number of motor tasks performed with a single arm. In the second half of the thesis book, two brain-computer interface systems to be used in rehabilitation scenarios with essential tremor patients and with patients with a stroke are proposed. In the first system, the electroencephalographic activity is used to anticipate voluntary movement actions, and this information is integrated in a multimodal platform estimating and suppressing the pathological tremors. In the second case, a conditioning paradigm for stroke patients based on the identification of the motor intention with temporal precision is presented and tested with a cohort of four patients along a month during which the patients undergo eight intervention sessions. The presented thesis has yielded advances from both the technological and the scientific points of view in all studies proposed. The main contributions from the technological point of view are: ¿ The design of an integrated upper-limb platform working in real-time. The platform was designed to acquire information from different types of noninvasive sensors (EEG, EMG and gyroscopic sensors) characterizing the planning and execution of voluntary movements. The platform was also capable of processing online the acquired data and generating an electrical feedback. ¿ The development of signal processing and classifying techniques adapted to the kind of signal recorded in the two kinds of patients considered in this thesis (patients with essential tremor and patients with a stroke) and to the requirements of online processing and real-time single-trial function desired for BCI applications. Especially in this regard, an original methodology to detect onsets of voluntary movements using slow cortical potentials and cortical rhythms has been presented. ¿ The design and validation in real-time of asynchronous BCI systems using motor planning EEG segments to anticipate or detect when patients begin a voluntary movement with the upper-limb. ¿ The proof of concept of the advantages of an EEG system integrated in a multimodal human-robot interface architecture that constitutes the first multimodal interface using the combined acquisition of EEG, EMG and gyroscopic data, which allows the concurrent characterization of different parts of the body associated with the execution of a movement. The main scientific contributions of this thesis are: ¿ The study of the EEG-based anticipation of voluntary movements presented in Chapter 5 of the thesis was the first demonstration (to the author's knowledge) of the capacity of the EEG signal to provide reliable movement predictions based on single-trial classification of online data of healthy subjects and ET patients. This study also provides, for the first time, the results of a BCI system tested in ET patients and it represents an original approach to BCI applications for this group of patients. ¿ It has been presented the first neurophysiological study using EEG and EMG data to analyze the effects of a drug on cortical activity and tremors of patients with ET. In addition, the obtained results have shown for the first time that a significant correlation exists between the dynamics of specific cortical oscillations and pathological tremor manifestation as a consequence of the drug effects. ¿ It has been proposed for the first time an experiment to inspect whether the EEG signal carries enough information to classify up to seven different tasks performed with a single limb. Both the methodology applied and the validation procedure are also innovative in this sort of studies. ¿ It has been demonstrated for the first time the relevance of combining different cortical sources of information (such as BP and ERD) to estimate the initiation of voluntary movements with the upper-limb. In this line, special relevance may be given to the positive results achieved with stroke patients, improving the results presented by similar previous EEG-based studies by other research groups. It has also been proposed for the first time an upper-limb intervention protocol for stroke patients using BP and ERD patterns to provide proprioceptive feedback tightly associated with the patients' expectations of movement. The effects of the proposed intervention have been studied with a small group of patients

NON INVASIVE INVESTIGATION OF SENSORIMOTOR CONTROL FOR FUTURE DEVELOPMENT OF BRAIN-MACHINE-INTERFACE (BMI)

My thesis focuses on describing novel functional connectivity properties of the sensorimotor system that are of potential interest in the field of brain-machine interface. In particular, I have investigated how the connectivity changes as a consequence of either pathologic conditions or spontaneous fluctuations of the brain's internal state. An ad-hoc electronic device has been developed to implement the appropriate experimental settings. First, the functional communication among sensorimotor primary nodes was investigated in multiple sclerosis patients afflicted by persistent fatigue. I selected this condition, for which there is no effective pharmacological treatment, since existing literature links this type of fatigue to the motor control system. In this study, electroencephalographic (EEG) and electromyographic (EMG) traces were acquired together with the pressure exerted on a bulb during an isometric hand grip. The results showed a higher frequency connection between central and peripheral nervous systems (CMC) and an overcorrection of the exerted movement in fatigued multiple sclerosis patients. In fact, even though any fatigue-dependent brain and muscular oscillatory activity alterations were absent, their connectivity worked at higher frequencies as fatigue increased, explaining 67% of the fatigue scale (MFIS) variance (p=.002). In other terms, the functional communication within the central-peripheral nervous systems, namely involving primary sensorimotor areas, was sensitive to tiny alterations in neural connectivity leading to fatigue, well before the appearance of impairments in single nodes of the network. The second study was about connectivity intended as propagation of information and studied in dependence on spontaneous fluctuations of the sensorimotor system triggered by an external stimulus. Knowledge of the propagation mechanisms and of their changes is essential to extract significant information from single trials. The EEG traces were acquired during transcranial magnetic stimulation (TMS) to yield to a deeper knowledge about the response to an external stimulation while the cortico-spinal system passes through different states. The results showed that spontaneous increases of the excitation of the node originating the transmission within the hand control network gave rise to dynamic recruitment patterns with opposite behaviors, weaker in homotopic and parietal circuits, stronger in frontal ones. As probed by TMS, this behavior indicates that the effective connectivity within bilateral circuits orchestrating hand control are dynamically modulated in time even in resting state. The third investigation assessed the plastic changes in the sensorimotor system after stroke induced by 3 months of robotic rehabilitation in chronic phase. A functional source extraction procedure was applied on the acquired EEG data, enabling the investigation of the functional connectivity between homologous areas in the resting state. The most significant result was that the clinical ameliorations were associated to a ‘normalization’ of the functional connectivity between homologous areas. In fact, the brain connectivity did not necessarily increase or decrease, but it settled within a ‘physiological’ range of connectivity. These studies strengthen our knowledge about the behavioral role of the functional connectivity among neuronal networks’ nodes, which will be essential in future developments of enhanced rehabilitative interventions, including brain-machine interfaces. The presented research also moves the definition of new indices of clinical state evaluation relevant for compensating interventions, a step forward

Challenges of neural interfaces for stroke motor rehabilitation

More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients

Generalized Cross-Frequency Decomposition: A Method for the Extraction of Neuronal Components Coupled at Different Frequencies

Perceptual, motor and cognitive processes are based on rich interactions between remote regions in the human brain. Such interactions can be carried out through phase synchronization of oscillatory signals. Neuronal synchronization has been primarily studied within the same frequency range, e.g., within alpha or beta frequency bands. Yet, recent research shows that neuronal populations can also demonstrate phase synchronization between different frequency ranges. An extraction of such cross-frequency interactions in EEG/MEG recordings remains, however, methodologically challenging. Here we present a new method for the robust extraction of cross-frequency phase-to-phase synchronized components. Generalized Cross-Frequency Decomposition (GCFD) reconstructs the time courses of synchronized neuronal components, their spatial filters and patterns. Our method extends the previous state of the art, Cross-Frequency Decomposition (CFD), to the whole range of frequencies: it works for any f1 and f2 whenever f1:f2 is a rational number. GCFD gives a compact description of non-linearly interacting neuronal sources on the basis of their cross-frequency phase coupling. We successfully validated the new method in simulations and tested it with real EEG recordings including resting state data and steady state visually evoked potentials (SSVEP)