390 research outputs found

    Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: a randomised pilot study

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    Objective. To compare neurological and functional outcomes between two groups of hospitalised patients with subacute tetraplegia. Approach. Seven patients received 20 sessions of brain computer interface (BCI) controlled functional electrical stimulation (FES) while five patients received the same number of sessions of passive FES for both hands. The neurological assessment measures were event related desynchronization (ERD) during movement attempt, Somatosensory evoked potential (SSEP) of the ulnar and median nerve; assessment of hand function involved the range of motion (ROM) of wrist and manual muscle test. Main results. Patients in both groups initially had intense ERD during movement attempt that was not restricted to the sensory-motor cortex. Following the treatment, ERD cortical activity restored towards the activity in able-bodied people in BCI-FES group only, remaining wide-spread in FES group. Likewise, SSEP returned in 3 patients in BCI-FES group, having no changes in FES group. The ROM of the wrist improved in both groups. Muscle strength significantly improved for both hands in BCI-FES group. For FES group, a significant improvement was noticed for right hand flexor muscles only. Significance. Combined BCI-FES therapy results in better neurological recovery and better improvement of muscle strength than FES alone. For spinal cord injured patients, BCI-FES should be considered as a therapeutic tool rather than solely a long-term assistive device for the restoration of a lost function

    Corticomuscular co-activation based hybrid brain-computer interface for motor recovery monitoring

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    The effect of corticomuscular coactivation based hybrid brain-computer interface (h-BCI) on post-stroke neurorehabilitation has not been explored yet. A major challenge in this area is to find an appropriate corticomuscular feature which can not only drive an h-BCI but also serve as a biomarker for motor recovery monitoring. Our previous study established the feasibility of a new method of measuring corticomuscular co-activation called correlation of band-limited power time-courses (CBPT) of EEG and EMG signals, outperforming the traditional EEG-EMG coherence in terms of accurately controlling a robotic hand exoskeleton device by the stroke patients. In this paper, we have evaluated the neurophysiological significance of CBPT for motor recovery monitoring by conducting a 5-week long longitudinal pilot trial on 4 chronic hemiparetic stroke patients. Results show that the CBPT variations correlated significantly (p-value< 0.05) with the dynamic changes in motor outcome measures during the therapy for all the patients. As the bandpower based biomarkers are popular in literature, a comparison with such biomarkers has also been made to cross-verify whether the changes in CBPT are indeed neurophysiological. Thus the study concludes that CBPT can serve as a biomarker for motor recovery monitoring while serving as a corticomuscular co-activation feature for h-BCI based neurorehabilitation. Despite an observed significant positive change between pre- and post-intervention motor outcomes, the question of the clinical effectiveness of CBPT is subject to further controlled trial on a larger cohort

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

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    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

    Neuromechanical Biomarkers for Robotic Neurorehabilitation

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    : One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the "biomarkers" that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the "Rehabilomics" has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective

    Brain-computer interface technology and neuroelectrical imaging to improve motor recovery after stroke

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    Stroke is defined as a focal lesion in the brain caused by acute ischemia or hemorrhage. The events that characterize acute stroke as well as the spontaneous recovery process occurring in the subacute phase, demonstrate that the focal damage affects remote interconnected areas. On the other hand, interconnected areas largely contribute to reorganization of the central nervous system (CNS) along the recovery process (plasticity) throughout compensatory or restorative mechanisms which can also lead to unwanted effects (maladaptive plasticity). Such post-stroke brain reorganization occurring spontaneously or within a rehabilitation program, is the object of wide literature in the fields of neuroimaging and neurophysiology. Brain-Computer Interfaces (BCIs) allow recognition, monitoring and reinforcement of specific brain activities as recorded eg. via electroencephalogram (EEG) and use such brain activity to control external devices via a computer. Sensorimotor rhythm (SMR) based BCIs exploit the modulation occurring in the EEG in response to motor imagery (MI) tasks: the subject is asked to perform MI of eg. left or right hand in order to control a cursor on a screen. In the context of post-stroke motor rehabilitation, such recruitment of brain activity within the motor system through MI can be used to harness brain reorganization towards a better functional outcome. Since 2009 my research activity has been focused mainly on BCI applications for upper limb motor rehabilitation after stroke within national (Ministry of Health) and international (EU) projects. I conducted (or participated to) several basic and clinical studies involving both healthy subjects and stroke patients and employing a combination of neurophysiological techniques (EEG, transcranial magnetic stimulation – TMS) and BCI technology (De Vico Fallani et al., 2013; Kaiser et al., 2012; Morone et al., 2015; Pichiorri et al., 2011). Such studies culminated in a randomized controlled trial (RCT) conducted on subacute stroke patients in which we demonstrated that a one-month training with a BCI system, which was specifically designed to support upper limb rehabilitation after stroke, significantly improved functional outcome (upper limb motor function) in the target population. Moreover, we observed changes in brain activity and connectivity (from high-density EEG recordings) occurring in motor related frequency ranges that significantly correlated to the functional outcome in the target group (Pichiorri et al., 2015). Following these promising results, my activity proceeded along two main pathways during the PhD course. On one hand, efforts were made ameliorate the prototypal BCI system used in (Pichiorri et al., 2015); the current system (called PromotƓr) is an all-in-one BCI training station with several improvements in usability for both the patient and the therapist (it is easier to use, employs wireless EEG system with reduced number of electrodes) (Colamarino et al., 2017a,b). The PromotƓr system is currently employed in add-on to standard rehabilitation therapy in patients admitted at Fondazione Santa Lucia. Preliminary results are available on chronic stroke patients, partially retracing those obtained in the subacute phase (Pichiorri et al., 2015) as well as explorative reports on patients with upper limb motor deficit of central origin other than stroke (eg. spinal cord injury at the cervical level). In the last year, I submitted research projects related to the PromotƓr system to private and public institutions. These projects foresee i) the addition of a proprioceptive feedback to the current visual one by means of Functional Electrical Stimulation (FES) ii) online evaluation of residual voluntary movement as recorded via electromyography (EMG), and iii) improvements in the BCI control features to integrate concepts derived from recent advancements in brain connectivity. On these themes, I recently obtained a grant from a private Swedish foundation. On the other hand, I conducted further analyses of data collected in the RCT (Pichiorri et al., 2015) to identify possible neurophysiological markers of good motor recovery. Specifically, I focused on interhemispheric connectivity (EEG derived) and its correlation with the integrity of the corticospinal tract (as assessed by TMS) and upper limb function (measured with clinical scales) in subacute stroke patients. The results of these analyses were recently published on an international peer-reviewed journal (Pichiorri et al., 2018). In the first chapter of this thesis, I will provide an updated overview on BCI application in neurorehabilitation (according to the current state-of-the-art). The content of this chapter is part of a wider book chapter, currently in press in Handbook of Clinical Neurology (Pichiorri and Mattia, in press). In the second chapter, I will report on the status of BCI applications for motor rehabilitation of the upper limb according to the approach I developed along my research activity, including ongoing projects and prliminary findings. In the third chapter I will present the results of a neurophysiological study on subacute stroke patients, exploring EEG derived interhemispheric connectivity as a possible neurophysiological correlate of corticospinal tract integrity and functional impairment of the upper limb. Overall this work aims to outline the current and potential role of BCI technology and EEG based neuroimaging in post-stroke rehabilitation mainly in relation to upper limb motor function, nonetheless touching upon possible different applications and contexts in neighboring research fields

    Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

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    Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a lockedin state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain- Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements

    Rehabilitation of gait after stroke: a review towards a top-down approach

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    This document provides a review of the techniques and therapies used in gait rehabilitation after stroke. It also examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity

    Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability

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    Appropriately combining mental practice (MP) and physical practice (PP) in a post-stroke rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here we present a rehabilitation protocol incorporating a separate active PP stage followed by MP stage, using a hand exoskeleton and brain-computer interface (BCI). The PP stage was mediated by a force sensor feedback based assist-as-needed control strategy, whereas the MP stage provided BCI based multimodal neurofeedback combining anthropomorphic visual feedback and proprioceptive feedback of the impaired hand extension attempt. A 6 week long clinical trial was conducted on 4 hemiparetic stroke patients (screened out of 16) with left hand disability. The primary outcome, motor functional recovery, was measured in terms of changes in Grip-Strength (GS) and Action Research Arm Test (ARAT) scores; whereas the secondary outcome, usability of the system, was measured in terms of changes in mood, fatigue and motivation on a visual-analog-scale (VAS). A positive rehabilitative outcome was found as the group mean changes from the baseline in the GS and ARAT were +6.38 kg and +5.66 accordingly. The VAS scale measurements also showed betterment in mood (-1.38), increased motivation (+2.10) and reduced fatigue (-0.98) as compared to the baseline. Thus the proposed neurorehabilitation protocol is found to be promising both in terms of clinical effectiveness and usability
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