Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients

Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome

Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.

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.

Nine decades of electrocorticography: A comparison between epidural and subdural recordings

In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.

Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients

Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.

The science and engineering behind sensitized brain-controlled bionic hands

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

Decoding ECoG signal into 3D hand translation using deep learning

Objective.Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise between invasiveness of the recording device and good spatial and temporal resolution of the recorded signal. However, most ECoG signal decoders used to predict continuous hand movements are linear models. These models have a limited representational capacity and may fail to capture the relationship between ECoG signal features and continuous hand movements. Deep learning (DL) models, which are state-of-the-art in many problems, could be a solution to better capture this relationship.Approach.In this study, we tested several DL-based architectures to predict imagined 3D continuous hand translation using time-frequency features extracted from ECoG signals. The dataset used in the analysis is a part of a long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was acquired during a closed-loop experiment with a tetraplegic subject. The proposed architectures include multilayer perceptron, convolutional neural networks (CNNs), and long short-term memory networks (LSTM). The accuracy of the DL-based and multilinear models was compared offline using cosine similarity.Main results.Our results show that CNN-based architectures outperform the current state-of-the-art multilinear model. The best architecture exploited the spatial correlation between neighboring electrodes with CNN and benefited from the sequential character of the desired hand trajectory by using LSTMs. Overall, DL increased the average cosine similarity, compared to the multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249 for the left and right hand, respectively.Significance.This study shows that DL-based models could increase the accuracy of BCI systems in the case of 3D hand translation prediction in a tetraplegic subject.

Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography

Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain–computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray–white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.

Neural decoding of gait phases during motor imagery and improvement of the decoding accuracy by concurrent action observation

Objective. Brain decoding of motor imagery (MI) not only is crucial for the control of neuroprosthesis but also provides insights into the underlying neural mechanisms. Walking consists of stance and swing phases, which are associated with different biomechanical and neural control features. However, previous knowledge on decoding the MI of gait is limited to simple information (e.g. the classification of 'walking' and 'rest').Approach. Here, we investigated the feasibility of electroencephalogram (EEG) decoding of the two gait phases during the MI of walking and whether the combined use of MI and action observation (AO) would improve decoding accuracy.Main results. We demonstrated that the stance and swing phases could be decoded from EEGs during MI or AO alone. We also demonstrated the decoding accuracy during MI was improved by concurrent AO. The decoding models indicated that the improved decoding accuracy following the combined use of MI and AO was facilitated by the additional information resulting from the concurrent cortical activations related to sensorimotor, visual, and action understanding systems associated with MI and AO.Significance. This study is the first to show that decoding the stance versus swing phases during MI is feasible. The current findings provide fundamental knowledge for neuroprosthetic design and gait rehabilitation, and they expand our understanding of the neural activity underlying AO, MI, and AO + MI of walking.Novelty and significanceBrain decoding of detailed gait-related information during motor imagery (MI) is important for brain-computer interfaces (BCIs) for gait rehabilitation. This study is the first to show the feasibility of EEG decoding of the stance versus swing phases during MI. We also demonstrated that the combined use of MI and action observation (AO) improves decoding accuracy, which is facilitated by the concurrent and synergistic involvement of the cortical activations for MI and AO. These findings extend the current understanding of neural activity and the combined effects of AO and MI and provide a basis for effective techniques for walking rehabilitation.

A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli

To date, traditional visual-based event-related potential brain-computer interface (ERP-BCI) systems continue to dominate the mainstream BCI research. However, these conventional BCIs are unsuitable for the individuals who have partly or completely lost their vision. Considering the poor performance of gaze independent ERP-BCIs, it is necessary to study techniques to improve the performance of these BCI systems. In this paper, we developed a novel 36-class bimodal ERP-BCI system based on tactile and auditory stimuli, in which six-virtual-direction audio files produced via head related transfer functions (HRTF) were delivered through headphones and location-congruent electro-tactile stimuli were simultaneously delivered to the corresponding position using electrodes placed on the abdomen and waist. We selected the eight best channels, trained a Bayesian linear discriminant analysis (BLDA) classifier and acquired the optimal trial number for target selection in online process. The average online information transfer rate (ITR) of the bimodal ERP-BCI reached 11.66 bit/min, improvements of 35.11% and 36.69% compared to the auditory (8.63 bit/min) and tactile approaches (8.53 bit/min), respectively. The results demonstrate the performance of the bimodal system is superior to each unimodal system. These facts indicate that the proposed bimodal system has potential utility as a gaze-independent BCI in future real-world applications.

Brain-Machine Neurofeedback: Robotics or Electrical Stimulation?

Neurotechnology such as brain-machine interfaces (BMI) are currently being investigated as training devices for neurorehabilitation, when active movements are no longer possible. When the hand is paralyzed following a stroke for example, a robotic orthosis, functional electrical stimulation (FES) or their combination may provide movement assistance; i.e., the corresponding sensory and proprioceptive neurofeedback is given contingent to the movement intention or imagination, thereby closing the sensorimotor loop. Controlling these devices may be challenging or even frustrating. Direct comparisons between these two feedback modalities (robotics vs. FES) with regard to the workload they pose for the user are, however, missing. Twenty healthy subjects controlled a BMI by kinesthetic motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the EEG beta frequency-band (17–21 Hz) was turned into passive opening of the contralateral hand by a robotic orthosis or FES in a randomized, cross-over block design. Mental demand, physical demand, temporal demand, performance, effort, and frustration level were captured with the NASA Task Load Index (NASA-TLX) questionnaire by comparing these workload components to each other (weights), evaluating them individually (ratings), and estimating the respective combinations (adjusted workload ratings). The findings were compared to the task-related aspects of active hand movement with EMG feedback. Furthermore, both feedback modalities were compared with regard to their BMI performance. Robotic and FES feedback had similar workloads when weighting and rating the different components. For both robotics and FES, mental demand was the most relevant component, and higher than during active movement with EMG feedback. The FES task led to significantly more physical (p = 0.0368) and less temporal demand (p = 0.0403) than the robotic task in the adjusted workload ratings. Notably, the FES task showed a physical demand 2.67 times closer to the EMG task, but a mental demand 6.79 times closer to the robotic task. On average, significantly more onsets were reached during the robotic as compared to the FES task (17.22 onsets, SD = 3.02 vs. 16.46, SD = 2.94 out of 20 opportunities; p = 0.016), even though there were no significant differences between the BMI classification accuracies of the conditions (p = 0.806; CI = −0.027 to −0.034). These findings may inform the design of neurorehabilitation interfaces toward human-centered hardware for a more natural bidirectional interaction and acceptance by the user.

Decoding Movement From Electrocorticographic Activity: A Review

Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.

μECoG Recordings Through a Thinned Skull

The studies described in this paper for the first time characterize the acute and chronic performance of optically transparent thin-film micro-electrocorticography (μECoG) grids implanted on a thinned skull as both an electrophysiological complement to existing thinned skull preparation for optical recordings/manipulations, and a less invasive alternative to epidural or subdurally placed μECoG arrays. In a longitudinal chronic study, μECoG grids placed on top of a thinned skull maintain impedances comparable to epidurally placed μECoG grids that are stable for periods of at least 1 month. Optogenetic activation of cortex is also reliably demonstrated through the optically transparent μECoG grids acutely placed on the thinned skull. Finally, spatially distinct electrophysiological recordings were evident on μECoG electrodes placed on a thinned skull separated by 500–750 μm, as assessed by stimulation evoked responses using optogenetic activation of cortex as well as invasive and epidermal stimulation of the sciatic and median nerve at chronic time points. Neural signals were collected through a thinned skull in mice and rats, demonstrating potential utility in neuroscience research applications such as in vivo imaging and optogenetics.

Human motor decoding from neural signals: a review

Many people suffer from movement disability due to amputation or neurological diseases. Fortunately, with modern neurotechnology now it is possible to intercept motor control signals at various points along the neural transduction pathway and use that to drive external devices for communication or control. Here we will review the latest developments in human motor decoding. We reviewed the various strategies to decode motor intention from human and their respective advantages and challenges. Neural control signals can be intercepted at various points in the neural signal transduction pathway, including the brain (electroencephalography, electrocorticography, intracortical recordings), the nerves (peripheral nerve recordings) and the muscles (electromyography). We systematically discussed the sites of signal acquisition, available neural features, signal processing techniques and decoding algorithms in each of these potential interception points. Examples of applications and the current state-of-the-art performance were also reviewed. Although great strides have been made in human motor decoding, we are still far away from achieving naturalistic and dexterous control like our native limbs. Concerted efforts from material scientists, electrical engineers, and healthcare professionals are needed to further advance the field and make the technology widely available in clinical use.

Brain-machine interfaces for rehabilitation in stroke: A review

BACKGROUND

Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement.

OBJECTIVE

This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke.

METHODS

We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces.

RESULTS

Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG).

CONCLUSIONS

Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.

Classification of different reaching movements from the same limb using EEG

OBJECTIVE

Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation.

APPROACH

Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared.

MAIN RESULTS

Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier.

SIGNIFICANCE

Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user’s intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control

This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm ². The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

A testbed for optimizing electrodes embedded in the skull or in artificial skull replacement pieces used after injury

BACKGROUND

Custom-fitted skull replacement pieces are often used after a head injury or surgery to replace damaged bone. Chronic brain recordings are beneficial after injury/surgery for monitoring brain health and seizure development. Embedding electrodes directly in these artificial skull replacement pieces would be a novel, low-risk way to perform chronic brain monitoring in these patients. Similarly, embedding electrodes directly in healthy skull would be a viable minimally-invasive option for many other neuroscience and neurotechnology applications requiring chronic brain recordings.

NEW METHOD

We demonstrate a preclinical testbed that can be used for refining electrode designs embedded in artificial skull replacement pieces or for embedding directly into the skull itself. Options are explored to increase the surface area of the contacts without increasing recording contact diameter to maximize recording resolution.

RESULTS

Embedding electrodes in real or artificial skull allows one to lower electrode impedance without increasing the recording contact diameter by making use of conductive channels that extend into the skull. The higher density of small contacts embedded in the artificial skull in this testbed enables one to optimize electrode spacing for use in real bone.

COMPARISON WITH EXISTING METHODS

For brain monitoring applications, skull-embedded electrodes fill a gap between electroencephalograms recorded on the scalp surface and the more invasive epidural or subdural electrode sheets.

CONCLUSIONS

Embedding electrodes into the skull or in skull replacement pieces may provide a safe, convenient, minimally-invasive alternative for chronic brain monitoring. The manufacturing methods described here will facilitate further testing of skull-embedded electrodes in animal models.

What Turns Assistive into Restorative Brain-Machine Interfaces?

Brain-machine interfaces (BMI) may support motor impaired patients during activities of daily living by controlling external devices such as prostheses (assistive BMI). Moreover, BMIs are applied in conjunction with robotic orthoses for rehabilitation of lost motor function via neurofeedback training (restorative BMI). Using assistive BMI in a rehabilitation context does not automatically turn them into restorative devices. This perspective article suggests key features of restorative BMI and provides the supporting evidence: In summary, BMI may be referred to as restorative tools when demonstrating subsequently (i) operant learning and progressive evolution of specific brain states/dynamics, (ii) correlated modulations of functional networks related to the therapeutic goal, (iii) subsequent improvement in a specific task, and (iv) an explicit correlation between the modulated brain dynamics and the achieved behavioral gains. Such findings would provide the rationale for translating BMI-based interventions into clinical settings for reinforcement learning and motor rehabilitation following stroke.

Towards decoding of functional movements from the same limb using EEG

In recent years, there has been an increasing interest in using electroencephalographic (EEG) activity to close the loop between brain oscillations and movement to induce functional motor rehabilitation. Rehabilitation robots or exoskeletons have been controlled using EEG activity. However, all studies have used a 2-class or one-dimensional decoding scheme. In this study we investigated EEG decoding of 5 functional movements of the same limb towards an online scenario. Six healthy participants performed a three-dimensional center-out reaching task based on direction movements (four directions and rest) wearing a 32-channel EEG cap. A BCI design based on multiclass extensions of Spectrally Weighted Common Spatial Patterns (Spec-CSP) and a linear discriminant analysis (LDA) classifier was developed and tested offline. The decoding accuracy was 5-fold cross-validated. A decoding accuracy of 39.5% on average for all the six subjects was obtained (chance level being 20%). The results of the current study demonstrate multiple functional movements decoding (significantly higher than chance level) from the same limb using EEG data. This study represents first steps towards a same limb multi degree of freedom (DOF) online EEG based BCI for motor restoration.

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton

Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients.

Reinforcement learning of self-regulated sensorimotor β-oscillations improves motor performance

Self-regulation of sensorimotor oscillations is currently researched in neurorehabilitation, e.g. for priming subsequent physiotherapy in stroke patients, and may be modulated by neurofeedback or transcranial brain stimulation. It has still to be demonstrated, however, whether and under which training conditions such brain self-regulation could also result in motor gains. Thirty-two right-handed, healthy subjects participated in a three-day intervention during which they performed 462 trials of kinesthetic motor-imagery while a brain-robot interface (BRI) turned event-related β-band desynchronization of the left sensorimotor cortex into the opening of the right hand by a robotic orthosis. Different training conditions were compared in a parallel-group design: (i) adaptive classifier thresholding and contingent feedback, (ii) adaptive classifier thresholding and non-contingent feedback, (iii) non-adaptive classifier thresholding and contingent feedback, and (iv) non-adaptive classifier thresholding and non-contingent feedback. We studied the task-related cortical physiology with electroencephalography and the behavioral performance in a subsequent isometric motor task. Contingent neurofeedback and adaptive classifier thresholding were critical for learning brain self-regulation which, in turn, led to behavioral gains after the intervention. The acquired skill for sustained sensorimotor β-desynchronization correlated significantly with subsequent motor improvement. Operant learning of brain self-regulation with a BRI may offer a therapeutic perspective for severely affected stroke patients lacking residual hand function.

Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site

BACKGROUND

Functional Electrical Stimulation (FES) is increasingly applied in neurorehabilitation. Particularly, the use of electrode arrays may allow for selective muscle recruitment. However, detecting the best electrode configuration constitutes still a challenge.

METHODS

A multi-contact set-up with thirty electrodes was applied for combined FES and electromyography (EMG) recording of the forearm. A search procedure scanned all electrode configurations by applying single, sub-threshold stimulation pulses while recording M-waves of the extensor digitorum communis (EDC), extensor carpi radialis (ECR) and extensor carpi ulnaris (ECU) muscles. The electrode contacts with the best electrophysiological response were then selected for stimulation with FES bursts while capturing finger/wrist extension and radial/ulnar deviation with a kinematic glove.

RESULTS

The stimulation electrodes chosen on the basis of M-waves of the EDC/ECR/ECU muscles were able to effectively elicit the respective finger/wrist movements for the targeted extension and/or deviation with high specificity in two different hand postures.

CONCLUSIONS

A subset of functionally relevant stimulation electrodes could be selected fast, automatic and non-painful from a multi-contact array on the basis of muscle responses to subthreshold stimulation pulses. The selectivity of muscle recruitment predicted the kinematic pattern. This electrophysiologically driven approach would thus allow for an operator-independent positioning of the electrode array in neurorehabilitation.

An Auditory-Tactile Visual Saccade-Independent P300 Brain–Computer Interface

Most P300 event-related potential (ERP)-based brain–computer interface (BCI) studies focus on gaze shift-dependent BCIs, which cannot be used by people who have lost voluntary eye movement. However, the performance of visual saccade-independent P300 BCIs is generally poor. To improve saccade-independent BCI performance, we propose a bimodal P300 BCI approach that simultaneously employs auditory and tactile stimuli. The proposed P300 BCI is a vision-independent system because no visual interaction is required of the user. Specifically, we designed a direction-congruent bimodal paradigm by randomly and simultaneously presenting auditory and tactile stimuli from the same direction. Furthermore, the channels and number of trials were tailored to each user to improve online performance. With 12 participants, the average online information transfer rate (ITR) of the bimodal approach improved by 45.43% and 51.05% over that attained, respectively, with the auditory and tactile approaches individually. Importantly, the average online ITR of the bimodal approach, including the break time between selections, reached 10.77 bits/min. These findings suggest that the proposed bimodal system holds promise as a practical visual saccade-independent P300 BCI.

The Evolution of Neuroprosthetic Interfaces

The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.

Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation

While robot-assisted arm and hand training after stroke allows for intensive task-oriented practice, it has provided only limited additional benefit over dose-matched physiotherapy up to now. These rehabilitation devices are possibly too supportive during the exercises. Neurophysiological signals might be one way of avoiding slacking and providing robotic support only when the brain is particularly responsive to peripheral input. We tested the feasibility of three-dimensional robotic assistance for reaching movements with a multi-joint exoskeleton during motor imagery (MI)-related desynchronization of sensorimotor oscillations in the β-band. We also registered task-related network changes of cortical functional connectivity by electroencephalography via the imaginary part of the coherence function. Healthy subjects and stroke survivors showed similar patterns—but different aptitudes—of controlling the robotic movement. All participants in this pilot study with nine healthy subjects and two stroke patients achieved their maximum performance during the early stages of the task. Robotic control was significantly higher and less variable when proprioceptive feedback was provided in addition to visual feedback, i.e., when the orthosis was actually attached to the subject’s arm during the task. A distributed cortical network of task-related coherent activity in the θ-band showed significant differences between healthy subjects and stroke patients as well as between early and late periods of the task. Brain-robot interfaces (BRIs) may successfully link three-dimensional robotic training to the participants’ efforts and allow for task-oriented practice of activities of daily living with a physiologically controlled multi-joint exoskeleton. Changes of cortical physiology during the task might also help to make subject-specific adjustments of task difficulty and guide adjunct interventions to facilitate motor learning for functional restoration, a proposal that warrants further investigation in a larger cohort of stroke patients.

Classifying multiple types of hand motions using electrocorticography during intraoperative awake craniotomy and seizure monitoring processes-case studies

In this work, some case studies were conducted to classify several kinds of hand motions from electrocorticography (ECoG) signals during intraoperative awake craniotomy & extraoperative seizure monitoring processes. Four subjects (P1, P2 with intractable epilepsy during seizure monitoring and P3, P4 with brain tumor during awake craniotomy) participated in the experiments. Subjects performed three types of hand motions (Grasp, Thumb-finger motion and Index-finger motion) contralateral to the motor cortex covered with ECoG electrodes. Two methods were used for signal processing. Method I: autoregressive (AR) model with burg method was applied to extract features, and additional waveform length (WL) feature has been considered, finally the linear discriminative analysis (LDA) was used as the classifier. Method II: stationary subspace analysis (SSA) was applied for data preprocessing, and the common spatial pattern (CSP) was used for feature extraction before LDA decoding process. Applying method I, the three-class accuracy of P1~P4 were 90.17, 96.00, 91.77, and 92.95% respectively. For method II, the three-class accuracy of P1~P4 were 72.00, 93.17, 95.22, and 90.36% respectively. This study verified the possibility of decoding multiple hand motion types during an awake craniotomy, which is the first step toward dexterous neuroprosthetic control during surgical implantation, in order to verify the optimal placement of electrodes. The accuracy during awake craniotomy was comparable to results during seizure monitoring. This study also indicated that ECoG was a promising approach for precise identification of eloquent cortex during awake craniotomy, and might form a promising BCI system that could benefit both patients and neurosurgeons.