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

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 Promotœr) 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 Promotœr 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 Promotœr 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

Spatial filters selection towards a rehabilitation BCI

Introducing BCI technology in supporting motor imagery (MI) training has revealed the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in stroke patients. To provide the most accurate and personalized feedback during the treatment, several stages of the electroencephalographic signal processing have to be optimized, including spatial filtering. This study focuses on data-independent approaches to optimize spatial filtering step. Specific aims were: i) assessment of spatial filters' performance in relation to the hand and foot scalp areas; ii) evaluation of simultaneous use of multiple spatial filters; iii) minimization of the number of electrodes needed for training. Our findings indicate that different spatial filters showed different performance related to the scalp areas considered. The simultaneous use of EEG signals conditioned with different spatial filters could either improve classification performance or, at same level of performance could lead to a reduction of the number of electrodes needed for successive training, thus improving usability of BCIs in clinical rehabilitation context

The Promotoer: a successful story of translational research in BCI for motor rehabilitation

Several groups have recently demonstrated in the context of randomized controlled trials (RCTs) how sensorimotor Brain-Computer Interface (BCI) systems can be beneficial for post-stroke motor recovery. Following a successful RCT, at Fondazione Santa Lucia (FSL) a further translational effort was made with the implementation of the Promotœr, an all in-one BCIsupported MI training station. Up to now, 25 patients underwent training with the Promotɶr during their admission for rehabilitation purposes (in add-on to standard therapy). Two illustrative cases are presented. Though currently limited to FSL, the Promotɶr represents a successful story of translational research in BCI for stroke rehabilitation. Results are promising both in terms of feasibility of a BCI training in the context of a real rehabilitation program and in terms of clinical and neurophysiological benefits observed in the patients

GUIDER: a GUI for semiautomatic, physiologically driven EEG feature selection for a rehabilitation BCI

GUIDER is a graphical user interface developed in MATLAB software environment to identify electroencephalography (EEG)-based brain computer interface (BCI) control features for a rehabilitation application (i.e. post-stroke motor imagery training). In this context, GUIDER aims to combine physiological and machine learning approaches. Indeed, GUIDER allows therapists to set parameters and constraints according to the rehabilitation principles (e.g. affected hemisphere, sensorimotor relevant frequencies) and foresees an automatic method to select the features among the defined subset. As a proof of concept, we compared offline performances between manual, just based on operator’s expertise and experience, and GUIDER semiautomatic features selection on BCI data collected from stroke patients during BCI-supported motor imagery training. Preliminary results suggest that this semiautomatic approach could be successfully applied to support the human selection reducing operator dependent variability in view of future multi-centric clinical trials

Neurophysiological constraints of control parameters for a brain computer interface system to support post-stroke motor rehabilitation

The Promotɶr is an all-in-one Brain Computer Interface (BCI)-system developed at Fondazione Santa Lucia (Rome, Italy) to support hand motor imagery practice after stroke. In this paper we focus on the optimization of control parameters for the BCI training. We compared two procedures for the feature selection: in the first, features were selected by means of a manual procedure (requiring “skilled users”), in the second a semiautomatic method, developed by us combining physiological and machine learning approaches, guided the feature selection. EEG-based BCI data set collected from 13 stroke patients were analyzed to the aim. No differences were found between the two procedures (paired-samples t-test, p=0.13). Results suggest that the semiautomatic procedure could be applied to support the manual feature selection, allowing no-skilled users to approach BCI technology for motor rehabilitation of stroke patients

Electroencephalography (EEG)-Derived Markers to Measure Components of Attention Processing

Although extensively studied for decades, attention system remains an interesting challenge in neuroscience field. The Attention Network Task (ANT) has been developed to provide a measure of the efficiency for the three attention components identified in the Posner’s theoretical model: alerting, orienting and executive control. Here we propose a study on 15 healthy subjects who performed the ANT. We combined advanced methods for connectivity estimation on electroencephalographic (EEG) signals and graph theory with the aim to identify neuro-physiological indices describing the most important features of the three networks correlated with behavioral performances. Our results provided a set of band-specific connectivity indices able to follow the behavioral task performances among subjects for each attention component as defined in the ANT paradigm. Extracted EEG-based indices could be employed in future clinical applications to support the behavioral assessment or to evaluate the influence of specific attention deficits on Brain Computer Interface (BCI) performance and/or the effects of BCI training in cognitive rehabilitation applications

Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients

Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts

Proof-of-Principle of a Brain-Computer Interface approach to support post-stroke arm rehabilitation in hospitalized patients: design, acceptability and usability

Objective To evaluate the feasibility of brain-computer interface (BCI)-assisted motor imagery training to support hand/arm motor rehabilitation after stroke during hospitalization. Design Proof-of-principle study. Setting Neurorehabilitation hospital. Participants Convenience sample of patients (N=8) with new-onset arm plegia or paresis caused by unilateral stroke. Interventions The BCI-based intervention was administered as an "add-on" to usual care and lasted 4 weeks. Under the supervision of a therapist, patients were asked to practice motor imagery of their affected hand and received as a discrete feedback the movements of a "virtual" hand superimposed on their own. Such a BCI-based device was installed in a rehabilitation hospital ward. Main Outcome Measures Following a user-centered design, we assessed system usability in terms of motivation, satisfaction (by means of visual analog scales), and workload (National Aeronautics and Space Administration-Task Load Index). The usability of the BCI-based system was also evaluated by 15 therapists who participated in a focus group. Results All patients successfully accomplished the BCI training. Significant positive correlations were found between satisfaction and motivation (P=.001, r=.393). BCI performance correlated with interest (P=.027, r=.257) and motivation (P=.012, r=.289). During the focus group, professionals positively acknowledged the opportunity offered by BCI-assisted training to measure patients' adherence to rehabilitation. Conclusions An ecological BCI-based device to assist motor imagery practice was found to be feasible as an add-on intervention and tolerable by patients who were exposed to the system in the rehabilitation environment. © 2015 American Congress of Rehabilitation Medicine