Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain–computer interfaces for motor rehabilitation in children

Case report: Novel use of clinical brain-computer interfaces in recreation programming for an autistic adolescent with co-occurring attention deficit hyperactivity disorder

Background

In recent years, several autistic children and youth have shown interest in Holland Bloorview Kids Rehabilitation Hospital's clinical brain computer interface (BCI) program. Existing literature about BCI use among autistic individuals has focused solely on cognitive skill development and remediation of challenging behaviors. To date, the benefits of recreational BCI programming with autistic children and youth have not been documented.

Purpose

This case report summarizes the experiences of an autistic male adolescent with co-occurring attention deficit hyperactivity disorder using a BCI for recreation and considers possible benefits with this novel user population.

Methods

A single retrospective chart review was completed with parental guardian's consent.

Findings

The participant demonstrated enjoyment in BCI sessions and requested continued opportunities to engage in BCI programming. This enjoyment correlated with improved Canadian Occupational Performance Measure (COPM) scores in BCI programming, outperforming scores from other recreational programs. Additionally, clinicians observed changes in social communication efforts and self-advocacy in this first autistic participant.

Conclusion

The use of brain computer interfaces in recreational programming provides a novel opportunity for engagement for autistic children and youth that may also support skill development.

EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review

OBJECTIVE

Therapeutic interventions for children and young people with dystonia and dystonic/dyskinetic cerebral palsy are limited. EEG-based neurofeedback is emerging as a neurorehabilitation tool. This scoping review maps research investigating EEG-based sensorimotor neurofeedback in adults and children with neurological motor impairments, including augmentative strategies.

METHODS

MEDLINE, CINAHL and Web of Science databases were searched up to 2023 for relevant studies. Study selection and data extraction were conducted independently by at least two reviewers.

RESULTS

Of 4380 identified studies, 133 were included, only three enrolling children. The most common diagnosis was adult-onset stroke (77%). Paradigms mostly involved upper limb motor imagery or motor attempt. Common neurofeedback modes included visual, haptic and/or electrical stimulation. EEG parameters varied widely and were often incompletely described. Two studies applied augmentative strategies. Outcome measures varied widely and included classification accuracy of the Brain-Computer Interface, degree of enhancement of mu rhythm modulation or other neurophysiological parameters, and clinical/motor outcome scores. Few studies investigated whether functional outcomes related specifically to the EEG-based neurofeedback.

CONCLUSIONS

There is limited evidence exploring EEG-based sensorimotor neurofeedback in individuals with movement disorders, especially in children. Further clarity of neurophysiological parameters is required to develop optimal paradigms for evaluating sensorimotor neurofeedback.

SIGNIFICANCE

The expanding field of sensorimotor neurofeedback offers exciting potential as a non-invasive therapy. However, this needs to be balanced by robust study design and detailed methodological reporting to ensure reproducibility and validation that clinical improvements relate to induced neurophysiological changes.

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date. In this manuscript, we discuss the ethical considerations related to the use of invasive brain-computer interface in children with severe neurologic disabilities. We first review the technical hardware and software considerations for the application of intracortical brain-computer interface in children. We then discuss ethical issues related to motor brain-computer interface use in pediatric neurosurgery. Finally, based on the input of a multidisciplinary panel of experts in fields related to brain-computer interface (functional and restorative neurosurgery, pediatric neurosurgery, mathematics and artificial intelligence research, neuroengineering, pediatric ethics, and pragmatic ethics), we then formulate initial recommendations regarding the clinical use of invasive brain-computer interfaces in children.

On the feasibility of simple brain-computer interface systems for enabling children with severe physical disabilities to explore independent movement

Introduction

Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI.

Materials and methods

Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated.

Results

All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 ± 12.1%, participants were more reliably able to activate (95.7 ± 11.3%) the device than hold still (62.1 ± 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 ± 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions.

Discussion

BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of “effective” BCI use. Participants exhibited PM skills that would categorize them as “emerging operational learners.” Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.

Past, Present, and Future of EEG-Based BCI Applications

An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.

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.

AOAR: an automatic ocular artifact removal approach for multi-channel electroencephalogram data based on non-negative matrix factorization and empirical mode decomposition

OBJECTIVE

Electroencephalogram (EEG) signals suffer inevitable interference from artifacts during the acquisition process. These artifacts make the analysis and interpretation of EEG data difficult. A major source of artifacts in EEGs is ocular activity. Therefore, it is important to remove ocular artifacts before further processing the EEG data.

APPROACH

In this study, an automatic ocular artifact removal (AOAR) method for EEG signals is proposed based on non-negative matrix factorization (NMF) and empirical mode decomposition (EMD). First, the amplitude of EEG data was normalized in order to ensure its non-negativity. Then, the normalized EEG data were decomposed into a set of components using NMF. The components containing ocular artifacts were extracted automatically through the fractal dimension. Subsequently, the temporal activities of these components were adaptively decomposed into some intrinsic mode functions (IMFs) by EMD. The IMFs corresponding to ocular artifacts were removed. Finally, the de-noised EEG data were reconstructed.

MAIN RESULTS

The proposed method was tested against seven other methods. In order to assess the effectiveness and reliability of the AOAR method in processing EEG data, experiments on ocular artifact removal were performed using simulated EEG data. Experimental results indicated that the proposed method was superior to the other methods in terms of root mean square error, signal-to-noise ratio (SNR) and correlation coefficient, especially in cases with a lower SNR. To further evaluate the potential applications of the proposed method in real life, the proposed method and others were applied to preprocess real EEG data recorded from children with and without attention-deficit/hyperactivity disorder (ADHD). After artifact rejection, the event-related potential feature was extracted for classification. The AOAR method was best at distinguishing the children with ADHD from the others.

SIGNIFICANCE

These results indicate that the proposed AOAR method has excellent prospects for removing ocular artifacts from EEG data.

EEG Assessment in a 2-Year-Old Child with Prolonged Disorders of Consciousness: 3 Years' Follow-up

A 2-year-old girl, diagnosed with traumatic brain injury and epilepsy following car trauma, was followed up for 3 years (a total of 15 recordings taken at 0, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 19, 26, and 35 months). There is still no clear guidance on the diagnosis, treatment, and prognosis of children with disorders of consciousness. At each appointment, recordings included the child's height, weight, pediatric Glasgow Coma Scale (pGCS), Coma Recovery Scale-Revised (CRS-R), Gesell Developmental Schedule, computed tomography or magnetic resonance imaging, electroencephalogram, frequency of seizures, oral antiepileptic drugs, stimulation with subject's own name (SON), and median nerve electrical stimulation (MNS). Growth and development were deemed appropriate for the age of the child. The pGCS and Gesell Developmental Schedule provided a comprehensive assessment of consciousness and mental development; the weighted Phase Lag Index (wPLI ) in the β-band (13–25 Hz) can distinguish unresponsive wakefulness syndrome from minimally conscious state and confirm that the SON and MNS were effective. The continuous increase of delta-band power indicates a poor prognosis. Interictal epileptiform discharges (IEDs) have a cumulative effect and seizures seriously affect the prognosis.

Advancing Brain-Computer Interface Applications for Severely Disabled Children Through a Multidisciplinary National Network: Summary of the Inaugural Pediatric BCI Canada Meeting

Thousands of youth suffering from acquired brain injury or other early-life neurological disease live, mature, and learn with only limited communication and interaction with their world. Such cognitively capable children are ideal candidates for brain-computer interfaces (BCI). While BCI systems are rapidly evolving, a fundamental gap exists between technological innovators and the patients and families who stand to benefit. Forays into translating BCI systems to children in recent years have revealed that kids can learn to operate simple BCI with proficiency akin to adults. BCI could bring significant boons to the lives of many children with severe physical impairment, supporting their complex physical and social needs. However, children have been neglected in BCI research and a collaborative BCI research community is required to unite and push pediatric BCI development forward. To this end, the pediatric BCI Canada collaborative network (BCI-CAN) was formed, under a unified goal to cooperatively drive forward pediatric BCI innovation and impact. This article reflects on the topics and discussions raised in the foundational BCI-CAN meeting held in Toronto, ON, Canada in November 2019 and suggests the next steps required to see BCI impact the lives of children with severe neurological disease and their families.

The performance of 9-11-year-old children using an SSVEP-based BCI for target selection

OBJECTIVE

In this paper, we report the performance of 9-11-year-old children using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) and provide control data collected from adults for comparison. Children in our study achieved a much higher performance (79% accuracy; average age 9.64 years old) than the only previous investigation of children using an SSVEP-based BCI (∼50% accuracy; average age 9.86 years old).

APPROACH

Experiments were conducted in two phases, a short calibration phase and a longer experimental phase. An offline analysis of the data collected during the calibration phase was used to set two parameters for a classifier and to screen participants who did not achieve a minimum accuracy of 85%.

MAIN RESULTS

Eleven of the 14 children and all 11 of the adults who completed the calibration phase met the minimum accuracy requirement. During the experimental phase, children selected targets with a similar accuracy (79% for children versus 78% for adults), latency (2.1 s for children versus 1.9 s for adults), and bitrate (0.50 bits s^-1 for children and 0.56 bits s^-1 for adults) as adults.

SIGNIFICANCE

This study shows that children can use an SSVEP-based BCI with higher performance than previously believed and is the first to report the performance of children using an SSVEP-based BCI in terms of latency and bitrate. The results of this study imply that children with severe motor disabilities (such as locked-in syndrome) may use an SSVEP-based BCI to restore/replace the ability to communicate.

Elucidating age-specific patterns from background electroencephalogram pediatric datasets via PARAFAC

Brain-computer interfaces (BCI) have the potential to provide non-muscular rehabilitation options for children. However, progressive changes in electrophysiology throughout development may pose a potential barrier in the translation of BCI rehabilitation schemes to children. Tensors and multiway analysis could provide tools which help characterize subtle developmental changes in electroencephalogram (EEG) profiles of children, thus supporting translation of BCI paradigms. Spatial, spectral and subject information of age-matched pediatric subjects in two EEG datasets were used to form 3-dimensional tensors for use in parallel factor analysis (PARAFAC) and direct projection comparison. Within dataset cross-validation results indicate PARAFAC can extract age-sensitive factors which accurately predict subject age in 90% of cases. Cross-dataset validation revealed extracted age-dependent factors correctly identified age in 3 of 4 test subjects. These findings demonstrate that tensor analysis can be applied to characterize the age-specific subtleties in EEG, which provide a means for tracking developmental changes in pediatric rehabilitation BCIs.