Brain-computer interface: current and emerging rehabilitation applications

Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review

This short review examines recent advancements in neurotechnologies within the context of managing unilateral spatial neglect (USN), a common condition following stroke. Despite the success of brain-computer interfaces (BCIs) in restoring motor function, there is a notable absence of effective BCI devices for treating cerebral visual impairments, a prevalent consequence of brain lesions that significantly hinders rehabilitation. This review analyzes current non-invasive BCIs and technological solutions dedicated to cognitive rehabilitation, with a focus on visuo-attentional disorders. We emphasize the need for further research into the use of BCIs for managing cognitive impairments and propose a new potential solution for USN rehabilitation, by combining the clinical subtleties of this syndrome with the technological advancements made in the field of neurotechnologies.

Review on brain-computer interface technologies in healthcare

Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs' potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.

Effectiveness of a Mindfulness Meditation App Based on an Electroencephalography-Based Brain-Computer Interface in Radiofrequency Catheter Ablation for Patients With Atrial Fibrillation: Pilot Randomized Controlled Trial

BACKGROUND

Radiofrequency catheter ablation (RFCA) for patients with atrial fibrillation (AF) can generate considerable physical and psychological discomfort under conscious sedation. App-based mindfulness meditation combined with an electroencephalography (EEG)-based brain-computer interface (BCI) shows promise as effective and accessible adjuncts in medical practice.

OBJECTIVE

This study aimed to investigate the effectiveness of a BCI-based mindfulness meditation app in improving the experience of patients with AF during RFCA.

METHODS

This single-center pilot randomized controlled trial involved 84 eligible patients with AF scheduled for RFCA, who were randomized 1:1 to the intervention and control groups. Both groups received a standardized RFCA procedure and a conscious sedative regimen. Patients in the control group were administered conventional care, while those in the intervention group received BCI-based app-delivered mindfulness meditation from a research nurse. The primary outcomes were the changes in the numeric rating scale, State Anxiety Inventory, and Brief Fatigue Inventory scores. Secondary outcomes were the differences in hemodynamic parameters (heart rate, blood pressure, and peripheral oxygen saturation), adverse events, patient-reported pain, and the doses of sedative drugs used in ablation.

RESULTS

BCI-based app-delivered mindfulness meditation, compared to conventional care, resulted in a significantly lower mean numeric rating scale (mean 4.6, SD 1.7 [app-based mindfulness meditation] vs mean 5.7, SD 2.1 [conventional care]; P=.008), State Anxiety Inventory (mean 36.7, SD 5.5 vs mean 42.3, SD 7.2; P<.001), and Brief Fatigue Inventory (mean 3.4, SD 2.3 vs mean 4.7, SD 2.2; P=.01) scores. No significant differences were observed in hemodynamic parameters or the amounts of parecoxib and dexmedetomidine used in RFCA between the 2 groups. The intervention group exhibited a significant decrease in fentanyl use compared to the control group, with a mean dose of 3.96 (SD 1.37) mcg/kg versus 4.85 (SD 1.25) mcg/kg in the control group (P=.003).The incidence of adverse events was lower in the intervention group (5/40) than in the control group (10/40), though this difference was not significant (P=.15).

CONCLUSIONS

BCI-based app-delivered mindfulness meditation effectively relieved physical and psychological discomfort and may reduce the doses of sedative medication used in RFCA for patients with AF.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05306015; https://clinicaltrials.gov/ct2/show/NCT05306015.

Brain-Computer Interfaces for Treatment of Focal Dystonia

Task-specificity in isolated focal dystonias is a powerful feature that may successfully be targeted with therapeutic brain-computer interfaces. While performing a symptomatic task, the patient actively modulates momentary brain activity (disorder signature) to match activity during an asymptomatic task (target signature), which is expected to translate into symptom reduction.

Brain-Computer Interface Priming for Cervical Transcutaneous Spinal Cord Stimulation Therapy: An Exploratory Case Study

Loss of arm and hand function is one of the most devastating consequences of cervical spinal cord injury (SCI). Although some residual functional neurons often pass the site of injury, recovery after SCI is extremely limited. Recent efforts have aimed to augment traditional rehabilitation by combining exercise-based training with techniques such as transcutaneous spinal cord stimulation (tSCS), and movement priming. Such methods have been linked with elevated corticospinal excitability, and enhanced neuroplastic effects following activity-based therapy. In the present study, we investigated the potential for facilitating tSCS-based exercise-training with brain-computer interface (BCI) motor priming. An individual with chronic AIS A cervical SCI with both sensory and motor complete tetraplegia participated in a two-phase cross-over intervention whereby they engaged in 15 sessions of intensive tSCS-mediated hand training for 1 h, 3 times/week, followed by a two week washout period, and a further 15 sessions of tSCS training with bimanual BCI motor priming preceding each session. We found using the Graded Redefined Assessment for Strength, Sensibility, and Prehension that the participant's arm and hand function improved considerably across each phase of the study: from 96/232 points at baseline, to 117/232 after tSCS training alone, and to 131/232 points after BCI priming with tSCS training, reflecting improved strength, sensation, and gross and fine motor skills. Improved motor scores and heightened perception to sharp sensations improved the neurological level of injury from C4 to C5 following training and improvements were generally maintained four weeks after the final training session. Although functional improvements were similar regardless of the presence of BCI priming, there was a moderate improvement of bilateral strength only when priming preceded tSCS training, perhaps suggesting a benefit of motor priming for tSCS training.

Establishing a Clinical Brain-Computer Interface Program for Children With Severe Neurological Disabilities

Background: Children with severe motor impairment but intact cognition are deprived of fundamental human rights. Quadriplegic cerebral palsy is the most common scenario where rehabilitation options remain limited. Brain-computer interfaces (BCI) represent a potential solution, but pediatric populations have been neglected. Direct engagement of children and families could provide meaningful opportunities while informing program development. We describe a patient-centered, clinical, non-invasive pediatric BCI program.

Methods: Eligible children were identified within a population-based, tertiary care children’s hospital. Criteria included 1) age six to 18 years, 2) severe physical disability (non-ambulatory, minimal hand use), 3) severely limited speech, and 4) evidence of grade 1 cognitive capacity. After initial screening for BCI competency, participants attended regular sessions, attempting commercially available and customized systems to play computer games, control devices, and attempt communication.

Results: We report the first 10 participants (median 11 years, range 6-16, 60% male). Over 334 hours of participation, there were no serious adverse events. BCI training was well tolerated, with favorable feedback from children and parents. All but one participant demonstrated the ability to perform BCI tasks. The majority performed well, using motor imagery based tasks for games and entertainment. Difficulties were most significant using P300, visual evoked potential based paradigms where maintenance of attention was challenging. Children and families expressed interest in continuing and informing program development.

Conclusions: Patient-centered clinical BCI programs are feasible for children with severe disabilities. Carefully selected participants can often learn quickly to perform meaningful tasks on readily available systems. Patient and family motivation and engagement appear high.

Effect of Cervical Transcutaneous Spinal Cord Stimulation on Sensorimotor Cortical Activity during Upper-Limb Movements in Healthy Individuals

Transcutaneous spinal cord stimulation (tSCS) can improve upper-limb motor function after spinal cord injury. A number of studies have attempted to deduce the corticospinal mechanisms which are modulated following tSCS, with many relying on transcranial magnetic stimulation to provide measures of corticospinal excitability. Other metrics, such as cortical oscillations, may provide an alternative and complementary perspective on the physiological effect of tSCS. Hence, the present study recorded EEG from 30 healthy volunteers to investigate if and how cortical oscillatory dynamics are altered by 10 min of continuous cervical tSCS. Participants performed repetitive upper-limb movements and resting-state tasks while tSCS was delivered to the posterior side of the neck as EEG was recorded simultaneously. The intensity of tSCS was tailored to each participant based on their maximum tolerance (mean: 50 ± 20 mA). A control session was conducted without tSCS. Changes to sensorimotor cortical activity during movement were quantified in terms of event-related (de)synchronisation (ERD/ERS). Our analysis revealed that, on a group level, there was no consistency in terms of the direction of ERD modulation during tSCS, nor was there a dose-effect between tSCS and ERD/ERS. Resting-state oscillatory power was compared before and after tSCS but no statistically significant difference was found in terms of alpha peak frequency or alpha power. However, participants who received the highest stimulation intensities had significantly weakened ERD/ERS (10% ERS) compared to when tSCS was not applied (25% ERD; p = 0.016), suggestive of cortical inhibition. Overall, our results demonstrated that a single 10 min session of tSCS delivered to the cervical region of the spine was not sufficient to induce consistent changes in sensorimotor cortical activity among the entire cohort. However, under high intensities there may be an inhibitory effect at the cortical level. Future work should investigate, with a larger sample size, the effect of session duration and tSCS intensity on cortical oscillations.

Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.

EEG-Based Multiword Imagined Speech Classification for Persian Words

This study focuses on providing a simple, extensible, and multiclass classifier for imagined words using EEG signals. Six Persian words, along with the silence (or idle state), were selected as input classes. The words can be used to control a mouse/robot movement or fill a simple computer form. The data set of this study was 10 recordings of five participants collected in five sessions. Each record had 20 repetitions of all words and the silence. Feature sets consist of normalized, 1 Hz resolution frequency spectrum of 19 EEG channels in 1 to 32 Hz bands. Majority rule on a bank of binary SVM classifiers was used to determine the corresponding class of a feature set. Mean accuracy and confusion matrix of the classifiers were estimated by Monte-Carlo cross-validation. According to recording the time difference of inter- and intraclass samples, three classification modes were defined. In the long-time mode, where all instances of a word in the whole database are involved, average accuracies were about 58% for Word-Silence, 60% for Word-Word, 40% for Word-Word-Silence, and 32% for the seven-class classification (6 Words+Silence). For the short-time mode, when only instances of the same record are used, the accuracies were 96, 75, 79, and 55%, respectively. Finally, in the mixed-time classification, where samples of every class are taken from a different record, the highest performance achieved with average accuracies was about 97, 97, 92, and 62%. These results, even in the worst case of the long-time mode, are meaningfully better than random and are comparable with the best reported results of previously conducted studies in this area.

Motor imagery and gait control in Parkinson's disease: techniques and new perspectives in neurorehabilitation

INTRODUCTION

Motor imagery (MI), defined as the ability to mentally represent an action without actual movement, has been used to improve motor function in athletes and, more recently, in neurological disorders such as Parkinson's disease (PD). Several studies have investigated the neural correlates of motor imagery, which change also depending on the action imagined.

AREAS COVERED

This review focuses on locomotion, which is a crucial activity in everyday life and is often impaired by neurological conditions. After a general discussion on the neural correlates of motor imagery and locomotion, we review the evidence highlighting the abnormalities in gait control and gait imagery in PD patients. Next, new perspectives and techniques for PD patients' rehabilitation are discussed, namely Brain Computer Interfaces (BCIs), neurofeedback, and virtual reality (VR).

EXPERT OPINION

Despite the few studies, the literature review supports the potential beneficial effects of motor imagery interventions in PD focused on locomotion. The development of new technologies could empower the administration of training based on motor imagery locomotor tasks, and their application could lead to new rehabilitation protocols aimed at improving walking ability in patients with PD.

Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke

Purpose

To observe the effect of a brain-computer interface-operated lower limb rehabilitation robot (BCI-LLRR) on functional recovery from stroke and to explore mechanisms.

Methods

Subacute-phase stroke patients were randomly divided into two groups. In addition to the routine intervention, patients in the treatment group trained on the BCI-LLRR and underwent the lower limb pedal training in the control group, both for the same time (30 min/day). All patients underwent assessment by instruments such as the National Institutes of Health Stroke Scale (NIHSS) and the Fugl-Meyer upper and lower limb motor function and balance tests, at 2 and 4 weeks of treatment and at 3 months after the end of treatment. Patients were also tested before treatment and after 4 weeks by leg motor evoked potential (MEP) and diffusion tensor imaging/tractography (DTI/DTT) of the head.

Results

After 4 weeks, the Fugl-Meyer leg function and NIHSS scores were significantly improved in the treatment group vs. controls (P < 0.01). At 3 months, further significant improvement was observed. The MEP amplitude and latency of the treatment group were significantly improved vs. controls. The effect of treatment on fractional anisotropy values was not significant.

Conclusions

The BCI-LLRR promoted leg functional recovery after stroke and improved activities of daily living, possibly by improving cerebral-cortex excitability and white matter connectivity.

Brain-Computer Interfaces and Creative Expression: Interface Considerations for Rehabilitative and Therapeutic Interactions

By translating brain signals into new kinds of outputs, Brain-Computer Interface (BCI) systems hold tremendous potential as both transformative rehabilitation and communication tools. BCIs can be considered a unique technology, in that they are able to provide a direct link between the brain and the external environment. By affording users with opportunities for communication and self-expression, BCI systems serve as a bridge between abled-bodied and disabled users, in turn reducing existing barriers between these groups. This perspective piece explores the complex shifting relationship between neuroadaptive systems and humans by foregrounding personal experience and embodied interaction as concepts through which to evaluate digital environments cultivated through the design of BCI interfaces. To underscore the importance of fostering human-centered experiences through technologically mediated interactions, this work offers a conceptual framework through which the rehabilitative and therapeutic possibilities of BCI user-system engagement could be furthered. By inviting somatic analysis towards the design of BCI interfaces and incorporating tenets of creative arts therapies practices into hybrid navigation paradigms for self-expressive applications, this work highlights the need for examining individual technological interactions as sites with meaning-making potentiality, as well as those conceived through unique exchanges based on user-specific needs for communication. Designing BCI interfaces in ways that afford users with increased options for navigation, as well as with the ability to share subjective and collective experiences, helps to redefine existing boundaries of digital and physical user-system interactions and encourages the reimagining of these systems as novel digital health tools for recovery.

Combined real-time fMRI and real time fNIRS brain computer interface (BCI): Training of volitional wrist extension after stroke, a case series pilot study

Objective

Pilot testing of real time functional magnetic resonance imaging (rt-fMRI) and real time functional near infrared spectroscopy (rt-fNIRS) as brain computer interface (BCI) neural feedback systems combined with motor learning for motor recovery in chronic severely impaired stroke survivors.

Approach

We enrolled a four-case series and administered three sequential rt-fMRI and ten rt-fNIRS neural feedback sessions interleaved with motor learning sessions. Measures were: Arm Motor Assessment Tool, functional domain (AMAT-F; 13 complex functional tasks), Fugl-Meyer arm coordination scale (FM); active wrist extension range of motion (ROM); volume of activation (fMRI); and fNIRS HbO concentration. Performance during neural feedback was assessed, in part, using percent successful brain modulations during rt-fNIRS.

Main results

Pre-/post-treatment mean clinically significant improvement in AMAT-F (.49 ± 0.22) and FM (10.0 ± 3.3); active wrist ROM improvement ranged from 20° to 50°. Baseline to follow-up change in brain signal was as follows: fMRI volume of activation was reduced in almost all ROIs for three subjects, and for one subject there was an increase or no change; fNIRS HbO was within normal range, except for one subject who increased beyond normal at post-treatment. During rt-fNIRS neural feedback training, there was successful brain signal modulation (42%–78%).

Significance

Severely impaired stroke survivors successfully engaged in spatially focused BCI systems, rt-fMRI and rt-fNIRS, to clinically significantly improve motor function. At the least, equivalency in motor recovery was demonstrated with prior long-duration motor learning studies (without neural feedback), indicating that no loss of motor improvement resulted from substituting neural feedback sessions for motor learning sessions. Given that the current neural feedback protocol did not prevent the motor improvements observed in other long duration studies, even in the presence of fewer sessions of motor learning in the current work, the results support further study of neural feedback and its potential for recovery of motor function in stroke survivors. In future work, expanding the sophistication of either or both rt-fMRI and rt-fNIRS could hold the potential for further reducing the number of hours of training needed and/or the degree of recovery.

ClinicalTrials.gov ID:NCT02856035.

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems

This paper presents an illusory visual motion stimulus-based brain-computer interface (BCI). We aim to use the proposed system to enhance the motor imagery (MI) modality. Since motor imagery requires a long time for training, a stimulation method with external stimuli through the sensory system is an alternative method for increasing efficiency. The research is divided into two parts. First, we observed the visual motion illusion pattern based on brain topographic maps for the novel BCI modality. Second, we implemented the illusory visual motion stimulus-based BCI system. Arrow and moving-arrow patterns were used to modulate alpha rhythms at the visual and motor cortex. The arrow pattern had an average classification accuracy of approximately 78.5%. Additionally, illusory visual motion stimulus-based BCI systems are proposed using the proposed feature extraction and decision-making algorithm. This proposed BCI system can control the cursor moving in the left or right direction with the designed algorithm to create five commands for assistive communication. Ten volunteers participated in the experiment, and a brain-computer interface system with motor imagery and an illusory visual motion stimulus were used to compare efficiencies. The results showed that the proposed method achieved approximately 4% higher accuracy than motor imagery. The accuracy of the proposed illusory visual motion stimulus and algorithm was approximately 80.3%. Therefore, an illusory visual motion stimulus hybrid BCI system can be incorporated into the MI-based BCI system for beginner motor imagery. Based on the results, the proposed assistive communication system can be used to enhance communication in people with severe disabilities.

Classification of EEG signals in epilepsy using a novel integrated TSK fuzzy system

The use of machine learning technology to recognize electrical signals of the brain is becoming increasingly popular. Compared with doctors’ manual judgment, machine learning methods are faster. However, only when its recognition accuracy reaches a high level can it be used in practice. Due to the difference in the data distributions of the training dataset and the test dataset and the lack of training samples, the classification accuracies of general machine learning algorithms are not satisfactory. In fact, among the many machine learning methods used to process epilepsy electroencephalogram (EEG) signals, most are black box methods; however, in medicine, methods with explanatory power are needed. In response to these three challenges, this paper proposes a novel technique based on domain adaptation learning, semi-supervised learning and a fuzzy system. In detail, we use domain adaptation learning to reduce deviation from the data distribution, semi-supervised learning to compensate for the lack of training samples, and the Takagi-Sugen-Kang (TSK) fuzzy system model to improve interpretability. Our experimental results show that the performance of the new method is better than those of most advanced epilepsy classification methods.

Development of a brain-computer interface for patients in the critical care setting

Objective

Behaviorally unresponsive patients in intensive care units (ICU) are unable to consistently and effectively communicate their most fundamental physical needs. Brain-Computer Interface (BCI) technology has been established in the clinical context, but faces challenges in the critical care environment. Contrary to cue-based BCIs, which allow activation only during pre-determined periods of time, self-paced BCI systems empower patients to interact with others at any time. The study aims to develop a self-paced BCI for patients in the intensive care unit.

Methods

BCI experiments were conducted in 18 ICU patients and 5 healthy volunteers. The proposed self-paced BCI system analyzes EEG activity from patients while these are asked to control a beeping tone by performing a motor task (i.e., opening and closing a hand). Signal decoding is performed in real time and auditory feedback given via headphones. Performance of the BCI system was judged based on correlation between the optimal and the observed performance.

Results

All 5 healthy volunteers were able to successfully perform the BCI task, compared to chance alone (p<0.001). 5 of 14 (36%) conscious ICU patients were able to perform the BCI task. One of these 5 patients was quadriplegic and controlled the BCI system without any hand movements. None of the 4 unconscious patients were able to perform the BCI task.

Conclusions

More than one third of conscious ICU patients and all healthy volunteers were able to gain control over the self-paced BCI system. The initial 4 unconscious patients were not. Future studies will focus on studying the ability of behaviorally unresponsive patients with cognitive motor dissociation to control the self-paced BCI system.

Therapeutic effects of brain-computer interface-controlled functional electrical stimulation training on balance and gait performance for stroke: A pilot randomized controlled trial

BACKGROUND

Brain-computer interface-controlled functional electrical stimulation (BCI-FES) approaches as new feedback training is increasingly being investigated for its usefulness in improving the health of adults or partially impaired upper extremity function in individuals with stroke.

OBJECTIVE

To evaluate the effects of BCI-FES on postural control and gait performance in individuals with chronic hemiparetic stroke.

METHODS

A total of 25 individuals with chronic hemiparetic stroke (13 individuals received BCI-FES and 12 individuals received functional electrical stimulation [FES]). The BCI-FES group received BCI-FES on the tibialis anterior muscle on the more-affected side for 30 minutes per session, 3 times per week for 5 weeks. The FES group received FES using the same methodology for the same periods. This study used the Mann-Whitney test to compare the two groups before and after training.

RESULTS

After training, gait velocity (mean value, 29.0 to 42.0 cm/s) (P = .002) and cadence (mean value, 65.2 to 78.9 steps/min) (P = .020) were significantly improved after BCI-FES training compared to those (mean value, 23.6 to 27.7 cm/s, and mean value, 59.4 to 65.5 steps/min, respectively) after FES approach. In the less-affected side, step length was significantly increased after BCI-FES (mean value, from 28.0 cm to 34.7 cm) more than that on FES approach (mean value, from 23.4 to 25.4 cm) (P = .031).

CONCLUSION

The results of the BCI-FES training shows potential advantages on walking abilities in individuals with chronic hemiparetic stroke.

Adaptive Brain-Computer Interface with Attention Alterations in Patients with Amyotrophic Lateral Sclerosis

The users' mental state such as attention variations can have an effect on the brain-computer interface (BCI) performance. In this project, we implemented an adaptive online BCI system with alterations in the users' attention. Twelve electroencephalography (EEG) signals were obtained from six patients with Amyotrophic Lateral Sclerosis (ALS). Participants were asked to execute 40 trials of ankle dorsiflexion concurrently with an auditory oddball task. EEG channels, classifiers and features with superior offline performance in the training phase of the classification of attention level were selected to use in the online mode for prediction the attention status. A feedback was provided to the users to reduce the amount of attention diversion created by the oddball task. The findings revealed that the users' attention can control an online BCI system and real-time neurofeedback can be applied to focus the attention of the user back onto the main task.

A soft and stretchable bilayer electrode array with independent functional layers for the next generation of brain machine interfaces

OBJECTIVE

Brain-Machine Interfaces (BMIs) hold great promises for advancing neuroprosthetics, robotics, and for providing treatment options for severe neurological diseases. The objective of this work is the development and in vivo evaluation of electrodes for BMIs that meet the needs to record brain activity at sub-millimeter resolution over a large area of the cortex while being soft and electromechanically robust (i.e. stretchable).

APPROACH

Current electrodes require a trade-off between high spatiotemporal resolution and cortical coverage area. To address the needs for simultaneous high resolution and large cortical coverage, the prototype electrode array developed in this study employs a novel bilayer routing of soft and stretchable lead wires from the recording sites on the surface of the brain (electrocorticography, ECoG) to the data acquisition system.

MAIN RESULTS

To validate the recording characteristics, the array was implanted in healthy felines for up to 5 months. Neural signals recorded from both layers of the device showed elevated mid-frequency structures typical of local field potential (LFP) signals that were stable in amplitude over implant duration, and also exhibited consistent frequency-dependent modulation after anesthesia induction by Telazol.

SIGNIFICANCE

The successful development of a soft and stretchable large-area, high resolution micro ECoG electrode array (lahrμECoG) is an important step to meet the neurotechnological needs of advanced BMI applications.

"Who Will I Be?": Relational Identity, Living with Amyotrophic Lateral Sclerosis, and Future-Oriented Decisionmaking

Patients with amyotrophic lateral sclerosis (ALS) face many difficult, timing-sensitive decisions over the course of their illness, weighing present versus future harms and benefits. Supplemented by interviews with people with ALS, we argue for a relational approach to understanding these decisions and their effects on identity. We highlight two critical aspects of the patient-caregiver relationship: (1) the extent to which each may rely on the other leaves their wellbeing intimately intertwined and (2) patients often require others to help with the imaginative task of considering possible futures for each therapeutic option. We show why family involvement in decisionmaking practices can be so critical, and shed light on the ways intimate others help preserve and protect people's identities amidst the destabilizing uncertainty illness and treatment can bring.

Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis

OBJECTIVE

The performance of brain-computer interface (BCI) systems is influenced by the user's mental state, such as attention diversion. In this study, we propose a novel online BCI system able to adapt with variations in the users' attention during real-time movement execution.

APPROACH

Electroencephalography signals were recorded from healthy participants and patients with Amyotrophic Lateral Sclerosis while attention to the target task (a dorsiflexion movement) was drifted using an auditory oddball task. For each participant, the selected channels, classifiers and features from a training data set were used in the online phase to predict the attention status.

MAIN RESULTS

For both healthy controls and patients, feedback to the user on attentional status reduced the amount of attention diversion.

SIGNIFICANCE

The findings presented here demonstrate successful monitoring of the users' attention in a fully online BCI system, and further, that real-time neurofeedback on the users' attention state can be implemented to focus the attention of the user back onto the main task.

Development of a combined, sequential real-time fMRI and fNIRS neurofeedback system to enhance motor learning after stroke

BACKGROUND

After stroke, wrist extension dyscoordination precludes functional arm/hand. We developed a more spatially precise brain signal for use in brain computer interface (BCI's) for stroke survivors.

NEW METHOD

Combination BCI protocol of real-time functional magnetic resonance imaging (rt-fMRI) sequentially followed by functional near infrared spectroscopy (rt-fNIRS) neurofeedback, interleaved with motor learning sessions without neural feedback. Custom Matlab and Python code was developed to provide rt-fNIRS-based feedback to the chronic stroke survivor, system user.

RESULTS

The user achieved a maximum of 71 % brain signal accuracy during rt-fNIRS neural training; progressive focus of brain activation across rt-fMRI neural training; increasing trend of brain signal amplitude during wrist extension across rt-fNIRS training; and clinically significant recovery of arm coordination and active wrist extension.

COMPARISON WITH EXISTING METHODS

Neurorehabilitation, peripherally directed, shows limited efficacy, as do EEG-based BCIs, for motor recovery of moderate/severely impaired stroke survivors. EEG-based BCIs are based on electrophysiological signal; whereas, rt-fMRI and rt-fNIRS are based on neurovascular signal.

CONCLUSION

The system functioned well during user testing. Methods are detailed for others' use. The system user successfully engaged rt-fMRI and rt-fNIRS neurofeedback systems, modulated brain signal during rt-fMRI and rt-fNIRS training, according to volume of brain activation and intensity of signal, respectively, and clinically significantly improved limb coordination and active wrist extension. fNIRS use in this case demonstrates a feasible/practical BCI system for further study with regard to use in chronic stroke rehab, and fMRI worked in concept, but cost and some patient-use issues make it less feasible for clinical practice.

Clinician awareness of brain computer interfaces: a Canadian national survey

Background

Individuals with severe neurological disabilities but preserved cognition, including children, are often precluded from connecting with their environments. Brain computer interfaces (BCI) are a potential solution where advancing technologies create new clinical opportunities. We evaluated clinician awareness as a modifiable barrier to progress and identified eligible populations.

Methods

We executed a national, population-based, cross-sectional survey of physician specialists caring for persons with severe disability. An evidence- and experience-based survey had three themes: clinician BCI knowledge, eligible populations, and potential impact. A BCI knowledge index was created and scored. Canadian adult and pediatric neurologists, physiatrists and a subset of developmental pediatricians were contacted. Secure, web-based software administered the survey via email with online data collection.

Results

Of 922 valid emails (664 neurologists, 253 physiatrists), 137 (15%) responded. One third estimated that ≥10% of their patients had severe neurological disability with cognitive capacity. BCI knowledge scores were low with > 40% identifying as less than “vaguely aware” and only 15% as “somewhat familiar” or better. Knowledge did not differ across specialties. Only 6 physicians (4%) had patients using BCI. Communication and wheelchair control rated highest for potentially improving quality of life. Most (81%) felt BCI had high potential to improve quality of life. Estimates suggested that > 13,000 Canadians (36 M population) might benefit from BCI technologies.

Conclusions

Despite high potential and thousands of patients who might benefit, BCI awareness among clinicians caring for disabled persons is poor. Further, functional priorities for BCI applications may differ between medical professionals and potential BCI users, perhaps reflecting that clinicians possess a less accurate understanding of the desires and needs of potential end-users. Improving knowledge and engaging both clinicians and patients could facilitate BCI program development to improve patient outcomes.

Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.

Happy emotion cognition of bimodal audiovisual stimuli optimizes the performance of the P300 speller

OBJECTIVE

Prior studies of emotional cognition have found that emotion-based bimodal face and voice stimuli can elicit larger event-related potential (ERP) amplitudes and enhance neural responses compared with visual-only emotional face stimuli. Recent studies on brain-computer interface have shown that emotional face stimuli have significantly improved the performance of the traditional P300 speller system, but its performance needs to be further improved for practical applications. Therefore, we herein propose a novel audiovisual P300 speller based on bimodal emotional cognition to further improve the performance of the P300 system.

METHODS

The audiovisual P300 speller we proposed is based on happy emotions, with visual and auditory stimuli that consist of several pairs of smiling faces and audible chuckles (E-AV spelling paradigm) of different ages and sexes. The control paradigm was the visual-only emotional face P300 speller (E-V spelling paradigm).

RESULTS

We compared the ERP amplitudes, accuracy, and raw bit rate between the E-AV and E-V spelling paradigms. The target stimuli elicited significantly increased P300 amplitudes (p < .05) and P600 amplitudes (p < .05) in the E-AV spelling paradigm compared with those in the E-V paradigm. The E-AV spelling paradigm also significantly improved the spelling accuracy and the raw bit rate compared with those in the E-V paradigm at one superposition (p < .05) and at two superpositions (p < .05).

SIGNIFICANCE

The proposed emotion-based audiovisual spelling paradigm not only significantly improves the performance of the P300 speller, but also provides a basis for the development of various bimodal P300 speller systems, which is a step forward in the clinical application of brain-computer interfaces.

Assessing Feedback Response With a Wearable Electroencephalography System

Background: Event related potential (ERP) components, such as P3, N2, and FRN, are potential metrics for assessing feedback response as a form of performance monitoring. Most research studies investigate these ERP components using clinical or research-grade electroencephalography (EEG) systems. Wearable EEGs, which are an affordable alternative, have the potential to assess feedback response using ERPs but have not been sufficiently evaluated. Feedback-related ERPs also have not been scientifically evaluated in interactive settings that are similar to daily computer use. In this study, a consumer-grade wearable EEG system was assessed for its feasibility to collect feedback-related ERPs through an interactive software module that provided an environment in which users were permitted to navigate freely within the program to make decisions. Methods: The recording hardware, which costs < $1,500 in total, incorporated the OpenBCI Cyton Board with Daisy chain, a consumer-grade EEG system that costs $949 USD. Seventeen participants interacted with an oddball paradigm and an interactive module designed to elicit feedback-related ERPs. The features of interests for the oddball paradigm were the P3 and N2 components. The features of interests for the interactive module were the P3, N2, and FRN components elicited in response to positive, neutral, and two types of negative feedback. The FRN was calculated by subtracting the positive feedback response from the negative feedback responses. Results: The P3 and N2 components of the oddball paradigm indicated statistically significant differences between infrequent targets and frequent targets which is in line with current literature. The P3 and N2 components elicited in the interactive module indicated statistically significant differences between positive, neutral, and negative feedback responses. There were no significant differences between the FRN types and significant interactions with channel group and FRN type. Conclusion: The OpenBCI Cyton, after some modifications, shows potential for eliciting and assessing P3, N2, and FRN components, which are important indicators for performance monitoring, in an interactive setting.

Using the Bioelectric Signals to Control of Wearable Orthosis of the Elbow Joint with Bi-Muscular Pneumatic Servo-Drive

This study presents a new design of a wearable orthosis of elbow joint with a bimuscular pneumatic servo-drive (PSD) with control based on the recording of bioelectric signals (BESs). The authors analyzed the impact of the induced brain activity and the muscular tension within the head of the participant on the BESs that can be used to control the PSD of the elbow joint orthosis. To control the elbow joint orthosis, a distributed control system (DCS) was developed, which contains two control layers: a master layer connected to the device for recording the BES and a direct layer contained in a wireless manner with the controller of the PSD. A kinematic-dynamic model of the elbow joint orthosis, patterned after the biological model of human biceps–triceps, was used in the programming of the PSD controller. A biomimetic dynamic model of the pneumatic muscle actuator (PMA) was used, in which the contraction force results from the adopted exponential static model of the pneumatic muscle (PM). The use of direct visual feedback (DVF) makes it possible for the participant to focus on the movement of the orthosis taking into account the motoric functions of the elbow.

A Radial Zoom Motion-Based Paradigm for Steady State Motion Visual Evoked Potentials

Background: In steady state visual evoked potential (SSVEP)-based brain-computer interfaces, prolonged repeated flicker stimulation would reduce the system performance. To reduce the visual discomfort and fatigue, while ensuring recognition accuracy, and information transmission rate (ITR), a novel motion paradigm based on the steady-state motion visual evoked potentials (SSMVEPs) is proposed.

Methods: The novel SSMVEP paradigm of the radial zoom motion was realized using the sinusoidal form to modulate the size of the stimuli. The radial zoom motion-based SSMVEP paradigm was compared with the flicker-based SSVEP paradigm and the SSMVEP paradigm based on Newton's ring motion. The canonical correlation analysis was used to identify the frequency of the eight targets, the recognition accuracy of different paradigms with different stimulation frequencies, and the ITR under different stimulation durations were calculated. The subjective comfort scores and fatigue scores, and decrease in the accuracy due to fatigue was evaluated.

Results: The average recognition accuracy of the novel radial zoom motion-based SSMVEP paradigm was 93.4%, and its ITR reached 42.5 bit/min, which was greater than the average recognition accuracy of the SSMVEP paradigm based on Newton's ring motion. The comfort score of the novel paradigm was greater than both the flicker-based SSVEP paradigm and SSMVEP paradigm based on Newton's ring motion. The decrease in the recognition accuracy due to fatigue was less than that of the SSSMVEP paradigm based on Newton's ring motion.

Conclusion: The SSMVEP paradigm based on radial zoom motion has high recognition accuracy and ITR with low visual discomfort and fatigue scores. The method has potential advantages in overcoming the performance decline caused by fatigue.

Uncovering Consciousness in Unresponsive ICU Patients: Technical, Medical and Ethical Considerations

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2019. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2019. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

WaveCSP: a robust motor imagery classifier for consumer EEG devices

There is an increasing demand for reliable motor imagery (MI) classification algorithms for applications in consumer level brain-computer interfacing (BCI). For the practical use, such algorithms must be robust to both device limitations and subject variability, which make MI classification a challenging task. This study proposes methods to study the effect of limitations including a limited number of electrodes, limited spatial distribution of electrodes, lower signal quality, subject variabilities and BCI literacy, on the performance of MI classification. To mitigate these limitations, we propose a machine learning approach, WaveCSP that uses 24 features extracted from EEG signals using wavelet transform and common spatial pattern (CSP) filtering techniques. The algorithm shows better performance in terms of subject variability compared to existing work. The application of WaveCSP to Physionet MI database shows more than 50% of the 109 subjects achieving accuracy higher than 64%. The data obtained from a commercial EEG headset using the same experimental protocol result in up to four out of five subjects who had prior BCI experience (out of a total of 25 subjects) performing with accuracy higher than 64%.

Inkjet assisted fabrication of planar biocompatible memristors

In this study we address a novel design of a planar memristor and investigate its biocompatibility. An experimental prototype of the proposed memristor assembly has been manufactured using a hybrid nanofabrication method, combining sputtering of electrodes, patterning the insulating trenches, and filling them with a memristive substance. To pattern the insulating trenches, we have examined two nanofabrication techniques employing either a focused ion beam or a cantilever tip of an atomic force microscope. Inkjet printing has been used to fill the trenches with the functional titania ink. The experimental prototypes have qualitatively demonstrated memristive current–voltage behavior, as well as high biocompatibility.

A planar memristor was fabricated by a hybrid method combining AFM patterning and inkjet printing.

Effects of simulated visual acuity and ocular motility impairments on SSVEP brain-computer interface performance: An experiment with Shuffle Speller

Individuals with severe speech and physical impairments may have concomitant visual acuity impairments (VAI) or ocular motility impairments (OMI) impacting visual BCI use. We report on the use of the Shuffle Speller typing interface for an SSVEP BCI copy-spelling task under three conditions: simulated VAI, simulated OMI, and unimpaired vision. To mitigate the effect of visual impairments, we introduce a method that adaptively selects a user-specific trial length to maximize expected information transfer rate (ITR); expected ITR is shown to closely approximate the rate of correct letter selections. All participants could type under the unimpaired and simulated VAI conditions, with no significant differences in typing accuracy or speed. Most participants (31 of 37) could not type under the simulated OMI condition; some achieved high accuracy but with slower typing speeds. Reported workload and discomfort were low, and satisfaction high, under the unimpaired and simulated VAI conditions. Implications and future directions to examine effect of visual impairment on BCI use is discussed.

Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification

A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means. Patients rated most communication sessions as difficult and unsuccessful. This, in turn, can cause distress, unrecognized pain, anxiety, and fear. As such, we designed a portable BCI system for ICU communications (BCI4ICU) optimized to operate effectively in an ICU environment. The system utilizes a wearable EEG cap coupled with an Android app designed on a mobile device that serves as visual stimuli and data processing module. Furthermore, to overcome the challenges that BCI systems face today in real-world scenarios, we propose a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm. First, we incorporate subject-specific information in the training phase of the SSVEP identification model using GMM-based training and adaptation. We evaluate subject-specific models against other subjects. Subsequently, from the GMM discriminative scores, we generate the transformed vectors, which are passed to our predictive model. Finally, the adapted mixture mean scores of the subject-specific GMMs are utilized to generate the high-dimensional supervectors. Our experimental results demonstrate that the proposed system achieved 98.7% average identification accuracy, which is promising in order to provide effective and consistent communication for patients in the intensive care.

Motor imagery learning across a sequence of trials in stroke patients

PURPOSE

In brain-computer interfaces (BCIs), electrical brain signals during motor imagery are utilized as commands connecting the brain to a computer. To use BCI in patients with stroke, unique brain signal changes should be characterized during motor imagery process. This study aimed to examine the trial-dependent motor-imagery-related activities in stroke patients.

METHODS

During the recording of electroencephalography (EEG) signals, 12 chronic stroke patients and 11 age-matched healthy controls performed motor imagery finger tapping at 1.3 sec intervals. Trial-dependent brain signal changes were assessed by analysis of the mu and beta bands.

RESULTS

Neuronal activity in healthy controls was observed over bilateral hemispheres at the mu and beta bands regardless of changes in the trials, whereas neuronal activity in stroke patients was mainly seen over the ipsilesional hemisphere at the beta band. With progression to repeated trials, healthy controls displayed a decrease in cortical activity in the contralateral hemisphere at the mu band and in bilateral hemispheres at the beta band. In contrast, stroke patients showed a decreasing trend in cortical activity only over the ipsilesional hemisphere at the beta band.

CONCLUSIONS

Trial-dependent changes during motor imagery learning presented in a different manner in stroke patients. Understanding motor imagery learning in stroke patients is crucial for enhancing the effectiveness of motor-imagery-based BCIs.

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System

Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

Brain-Computer Interface for Clinical Purposes: Cognitive Assessment and Rehabilitation

Alongside the best-known applications of brain-computer interface (BCI) technology for restoring communication abilities and controlling external devices, we present the state of the art of BCI use for cognitive assessment and training purposes. We first describe some preliminary attempts to develop verbal-motor free BCI-based tests for evaluating specific or multiple cognitive domains in patients with Amyotrophic Lateral Sclerosis, disorders of consciousness, and other neurological diseases. Then we present the more heterogeneous and advanced field of BCI-based cognitive training, which has its roots in the context of neurofeedback therapy and addresses patients with neurological developmental disorders (autism spectrum disorder and attention-deficit/hyperactivity disorder), stroke patients, and elderly subjects. We discuss some advantages of BCI for both assessment and training purposes, the former concerning the possibility of longitudinally and reliably evaluating cognitive functions in patients with severe motor disabilities, the latter regarding the possibility of enhancing patients' motivation and engagement for improving neural plasticity. Finally, we discuss some present and future challenges in the BCI use for the described purposes.

Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery

BACKGROUND

Biomarkers derived from neural activity of the brain present a vital tool for the prediction and evaluation of post-stroke motor recovery, as well as for real-time biofeedback opportunities.

METHODS

In order to encapsulate recovery-related reorganization of brain networks into such biomarkers, we have utilized the generalized measure of association (GMA) and graph analyses, which include global and local efficiency, as well as hemispheric interdensity and intradensity. These methods were applied to electroencephalogram (EEG) data recorded during a study of 30 stroke survivors (21 male, mean age 57.9 years, mean stroke duration 22.4 months) undergoing 12 weeks of intensive therapeutic intervention.

RESULTS

We observed that decreases of the intradensity of the unaffected hemisphere are correlated (r s =-0.46;p<0.05) with functional recovery, as measured by the upper-extremity portion of the Fugl-Meyer Assessment (FMUE). In addition, high initial values of local efficiency predict greater improvement in FMUE (R 2=0.16;p<0.05). In a subset of 17 subjects possessing lesions of the cerebral cortex, reductions of global and local efficiency, as well as the intradensity of the unaffected hemisphere are found to be associated with functional improvement (r s =-0.60,-0.66,-0.75;p<0.05). Within the same subgroup, high initial values of global and local efficiency, are predictive of improved recovery (R 2=0.24,0.25;p<0.05). All significant findings were specific to the 12.5-25 Hz band.

CONCLUSIONS

These topological measures show promise for prognosis and evaluation of therapeutic outcomes, as well as potential application to BCI-enabled biofeedback.

Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future

The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

Toward a P300 Based Brain-Computer Interface for Aphasia Rehabilitation after Stroke: Presentation of Theoretical Considerations and a Pilot Feasibility Study

People with post-stroke motor aphasia know what they would like to say but cannot express it through motor pathways due to disruption of cortical circuits. We present a theoretical background for our hypothesized connection between attention and aphasia rehabilitation and suggest why in this context, Brain-Computer Interface (BCI) use might be beneficial for patients diagnosed with aphasia. Not only could BCI technology provide a communication tool, it might support neuronal plasticity by activating language circuits and thereby boost aphasia recovery. However, stroke may lead to heterogeneous symptoms that might hinder BCI use, which is why the feasibility of this approach needs to be investigated first. In this pilot study, we included five participants diagnosed with post-stroke aphasia. Four participants were initially unable to use the visual P300 speller paradigm. By adjusting the paradigm to their needs, participants could successfully learn to use the speller for communication with accuracies up to 100%. We describe necessary adjustments to the paradigm and present future steps to investigate further this approach.

SOLICITING BCI USER EXPERIENCE FEEDBACK FROM PEOPLE WITH SEVERE SPEECH AND PHYSICAL IMPAIRMENTS

Brain-computer interface (BCI) researchers have shown increasing interest in soliciting user experience (UX) feedback, but the severe speech and physical impairments (SSPI) of potential users create barriers to effective implementation with existing feedback instruments. This article describes augmentative and alternative communication (AAC)-based techniques for obtaining feedback from this population, and presents results from administration of a modified questionnaire to 12 individuals with SSPI after trials with a BCI spelling system. The proposed techniques facilitated successful questionnaire completion and provision of narrative feedback for all participants. Questionnaire administration required less than five minutes and minimal effort from participants. Results indicated that individual users may have very different reactions to the same system, and that ratings of workload and comfort provide important information not available through objective performance measures. People with SSPI are critical stakeholders in the future development of BCI, and appropriate adaptation of feedback questionnaires and administration techniques allows them to participate in shaping this assistive technology.

Re-establishing the disrupted sensorimotor loop in deafferented and deefferented people: The case of spinal cord injuries

Acting efficiently in the world depends on the activity of motor and somatosensory systems, the integration of which is necessary for the proper functioning of the sensorimotor loop (SL). Profound alterations of SL functioning follow spinal cord injury (SCI), a condition that brings about a disconnection of the body from the brain. Such disconnection creates a substantial deprivation of somatosensorial inputs and motor outputs. Consequent somatic deficits and motor paralysis affect the body below the lesion level. A complete restoration of normal functions of the SL cannot be expected until basic neuroscience has found a way to re-establish the interrupted neural connectivity. Meanwhile, studies should focus on the development of technical solutions for dealing with the disruption of the sensorimotor loop. This review discusses the structural and functional adaptive reorganization of the brain after SCI, and the maladaptive mechanisms that impact on the processing of body related information, which alter motor imagery strategies and EEG signals. Studies that show how residual functions (e.g. face tactile sensitivity) may help people to restore a normal body image are also reviewed. Finally, data on how brain and residual body signals may be used to improve brain computer interface systems is discussed in relation to the issue of how such systems may help SCI people to re-enter the world and interact with objects and other individuals.