Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective. A search for January 2010–October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures. 30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery. 19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.

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Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

Transformation rates of naphthalene, phenanthrene, and benzo[a]pyrene in oxidized surficial sediments of a polluted urban estuary, Boston Harbor, Mass., were determined over a period of 15 months. Three sites characterized by muddy sediments were selected to represent a >300-fold range of ambient polycyclic aromatic hydrocarbon (PAH) concentration. Transformation rates were determined by a trace-level radiolabel PAH assay which accounted for PAH mineralization, the formation of polar metabolites, residue, and recovered parental PAHs in sediment slurries. Transformation rates of the model PAHs increased with increasing ambient PAH concentrations. However, turnover times for a given PAH were similar at all sites. The turnover times were as follows: naphthalene, 13.2 to 20.1 days; phenanthrene, 7.9 to 19.8 days, and benzo[a]pyrene, 53.7 to 82.3 days. At specific sites, rates were significantly affected by salinity, occasionally affected by temperature, but not affected by pH over the course of the study. Seasonal patterns of mineralization were observed for each of the PAHs at all sites. The timing of seasonal maxima of PAH mineralization varied from site to site. Seasonal potential heterotrophic activities as measured by acetate and glutamate mineralization rates did not always coincide with PAH mineralization maxima and minima, suggesting that the two processes are uncoupled in estuarine sediments. Images

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Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Objective We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. Approach 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). Main results By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. Significance These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training.

https://iopscience.iop.org/article/10.1088/1741-2552/ab377d/pdf

A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery.

Visual information plays an essential role in enhancing neural activity during mental practices. Previous research has shown that using different visual scenarios during mental practices that involve imagining the movement of a specific body part may result in differences in performance. Many of these scenarios utilize the concept of embodiment, or one’s observation of another entity to be a part of oneself, to improve practice quality of the imagined body movement. We therefore hypothesized that applying immersive virtual reality headsets, with their ability to provide rich immersion and illusion by presenting egocentrically simulated virtual scenarios, and action observation to motor imagery practice will result in significant improvement. To explore the possible synergy between immersive systems and motor imagery, we analyzed the electroencephalogram signals of our participants as they were presented the same virtual hand movement scenario with two different mediums: an immersive virtual reality headset and a monitor display. Our experimental results provide evidence that the immersive virtual reality headsets induced improved rhythmic patterns with better discriminating spatial features from the brain compared to the monitor display. These findings suggest that the use of immersive virtual reality headsets, with the illusion and embodiment they provide, can effectively improve motor imagery training.

Observing Actions Through Immersive Virtual Reality Enhances Motor Imagery Training

Improving performance in motor imagery BCI-based control applications via virtually embodied feedback.

Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback

We have developed a brain-machine interface (BMI) rehabilitation system for patients with stroke and motor paralysis, which provides proprioceptive feedback upon successful generation of motor-imagery (MI)-induced event-related desynchronization (ERD) and a decrease in mu band (8–13 Hz) activity derived from hand motor imagery. This system consists of an electroencephalogram (EEG) ampli er operated using the MATLAB Simulink software; a pneumatic robotic exoskeleton to provide proprioceptive feedback to the paralyzed hand; and a tablet computer placed over the paralyzed hand to display a hand-action movie to facilitate ERD generation. The EEG ampli er was connected and synchronized via the exoskeleton and tablet computer with an Arduino microcomputer. Nine patients in the subacute stage of recovery after stroke participated in a neurofeedback training experiment, which employed the aforementioned system. During the 4 weeks of this study, the participants received 2 weeks of BMI-based or control interventions in a random and counterbalanced order, in addition to their daily conventional physiotherapy. The control intervention consisted of the same MI training as the BMI-based intervention, but the exoskeleton always provided proprioceptive feedback regardless of the ERD strength. The ERD strength in the affected hemisphere showed a desirable increase with a signi cant improvement of nger joint spasticity, only after the DMB-based intervention period, and not after the control intervention period. The proposed neurofeedback training can help patients with stroke and movement disorders, because increased ERD strength may lead to recovery of motor function.

Development of a Brain-machine Interface for Stroke Rehabilitation Using Event-related Desynchronization and Proprioceptive Feedback

We developed a low-cost, event-related desynchronization (ERD) measurement system using consumer-grade EEG system (Emotiv Epoc) for application of motor-imagery (MI) related brain machine interface (BMI). We investigated accuracy of ERD power detection of Epoc by comparing the ERD power calculated from simultaneously recorded EEG via Epoc and medical-grade EEG system (g.USBamp: g.tec medical engineering). Eleven healthy participants performed MI of holding a rolling tennis ball and opening the grasped hand under the observation of hand movement video. The mean ERD power was comparable between Epoc and g. USBamp with both hand gestures tested. ERD detection accuracy of Epoc was 70.5% of the all trials tested. Considering the price of Epoc, our results suggest that Epoc could be a good substitute for medical-grade EEG systems for the purpose of MI-based BMI. Keyword: BMI, Emotiv, event-related desynchronization

https://www.jstage.jst.go.jp/article/jsmbe/55Annual/Proc/55Annual_556/_pdf

Development of an ERD measurement system using Emotiv Epoc

We are developing Digital Mirror Box (DMB), a brain machine interface (BMI)based rehabilitation system for stroke patients with hand paralysis. Aiming to train the motor-command generation from the affected hemisphere, DMB uses an event related desynchronization (ERD) derived from motor imagery as an operating signal of the BMI feedback. In order to assist the patients to perform motor imagery, the current DMB system presents a movie of hand motion, in which a hand-grasping motion picture is presented immediately after a still image of the fixation point. However, there remains a possibility that the detected ERD is influenced by an attentional switching from still to motion picture, not by action observation or motor imagery per se. We therefore modified the movie to contain a still image of a resting hand between fixation cue and hand motion picture. We could successfully differentiate the μ band suppression of the attentional switching from those derived from motor imagery.

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Investigation of optimal visual cue for training of motor-imagery response

We investigated the transition of event-related desynchronization (ERD) intensity through 6 days of brain-machine interface (BMI) training using Digital Mirror Box (DMB), which is a potential rehabilitation system for stroke patients with hand paralysis. Eleven healthy participants performed motor-imagery of grasping their dominant hand under the observation of hand movement video. The overall ERD strength of all participants showed a significant increase from day 1 to day 6. When the participants were divided into high and low ERD groups by their initial ERD strength of higher or lower than 0% at the initial measurement before BMI training, respectively, participants in low ERD group showed larger training effect. These results suggest that BMI training is particularly useful for increasing the ERD strength of stroke patients who have lower ERD strength.

/pdf/effect-of-neurofeedback-training-on-event-related-3khesf9uro.pdf

Masaya Kurata

Papers

Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback

Development of a Brain-machine Interface for Stroke Rehabilitation Using Event-related Desynchronization and Proprioceptive Feedback

Development of an ERD measurement system using Emotiv Epoc

Investigation of optimal visual cue for training of motor-imagery response

Effect of neurofeedback training on event-related desynchronization strength by motor imagery