Brain-computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum

A Randomized Trial Utilizing EEG Brain Computer Interface to Improve Facial Emotion Recognition in Autistic Adults

PURPOSE

Many individuals with autism spectrum disorder (ASD) experience challenges with facial emotion recognition (FER), which may exacerbate social difficulties in ASD. Few studies have examined whether FER can be experimentally manipulated and improved for autistic people. This study utilized a randomized controlled trial design to examine acceptability and preliminary clinical impact of a novel mixed reality-based neurofeedback program, FER Assistant, using EEG brain computer interface (BCI)-assisted technology to improve FER for autistic adolescents and adults.

METHODS

Twenty-seven autistic male participants (M age: 21.12 years; M IQ: 105.78; 85% white) were randomized to the active condition to receive FER Assistant (n = 17) or waitlist control (n = 10). FER Assistant participants received ten sessions utilizing BCI-assisted neurofeedback training in FER. All participants, regardless of randomization, completed a computerized FER task at baseline and endpoint.

RESULTS

Results partially indicated that FER Assistant was acceptable to participants. Regression analyses demonstrated that participation in FER Assistant led to group differences in FER at endpoint, compared to a waitlist control. However, analyses examining reliable change in FER indicated no reliable improvement or decline for FER Assistant participants, whereas two waitlist participants demonstrated reliable decline.

CONCLUSION

Given the preliminary nature of this work, results collectively suggest that FER Assistant may be an acceptable intervention. Results also suggest that FER may be a potential mechanism that is amenable to intervention for autistic individuals, although additional trials using larger sample sizes are warranted.

EEG decoding for effects of visual joint attention training on ASD patients with interpretable and lightweight convolutional neural network

Visual joint attention, the ability to track gaze and recognize intent, plays a key role in the development of social and language skills in health humans, which is performed abnormally hard in autism spectrum disorder (ASD). The traditional convolutional neural network, EEGnet, is an effective model for decoding technology, but few studies have utilized this model to address attentional training in ASD patients. In this study, EEGNet was used to decode the P300 signal elicited by training and the saliency map method was used to visualize the cognitive properties of ASD patients during visual attention. The results showed that in the spatial distribution, the parietal lobe was the main region of classification contribution, especially for Pz electrode. In the temporal information, the time period from 300 to 500 ms produced the greatest contribution to the electroencephalogram (EEG) classification, especially around 300 ms. After training for ASD patients, the gradient contribution was significantly enhanced at 300 ms, which was effective only in social scenarios. Meanwhile, with the increase of joint attention training, the P300 latency of ASD patients gradually shifted forward in social scenarios, but this phenomenon was not obvious in non-social scenarios. Our results indicated that joint attention training could improve the cognitive ability and responsiveness of social characteristics in ASD patients.

Intracranial Neurofeedback Modulating Neural Activity in the Mesial Temporal Lobe During Memory Encoding: A Pilot Study

Removal of the mesial temporal lobe (MTL) is an established surgical procedure that leads to seizure freedom in patients with intractable MTL epilepsy; however, it carries the potential risk of memory damage. Neurofeedback (NF), which regulates brain function by converting brain activity into perceptible information and providing feedback, has attracted considerable attention in recent years for its potential as a novel complementary treatment for many neurological disorders. However, no research has attempted to artificially reorganize memory functions by applying NF before resective surgery to preserve memory functions. Thus, this study aimed (1) to construct a memory NF system that used intracranial electrodes to feedback neural activity on the language-dominant side of the MTL during memory encoding and (2) to verify whether neural activity and memory function in the MTL change with NF training. Two intractable epilepsy patients with implanted intracranial electrodes underwent at least five sessions of memory NF training to increase the theta power in the MTL. There was an increase in theta power and a decrease in fast beta and gamma powers in one of the patients in the late stage of memory NF sessions. NF signals were not correlated with memory function. Despite its limitations as a pilot study, to our best knowledge, this study is the first to report that intracranial NF may modulate neural activity in the MTL, which is involved in memory encoding. The findings provide important insights into the future development of NF systems for the artificial reorganization of memory functions.

Attachment style, emotional feedback, and neural processing: investigating the influence of attachment on the P200 and P400 components of event-related potentials

Understanding the interplay between attachment style, emotional processing, and neural responses is crucial for comprehending the diverse ways individuals function socially and emotionally. While previous research has contributed to our knowledge of how attachment style influences emotional processing, there is still a gap in the literature when it comes to investigating emotional feedback using event-related potentials (ERPs) within a cognitive framework. This study aims to address this gap by examining the effects of attachment style and feedback valence on ERP components, specifically focusing on the P200 and P400. The findings reveal significant effects of attachment style and feedback valence on both components. In insecure attachment styles, noticeable shifts in relative energy are observed during the transition from negative to positive feedback for both the P200 and P400. Conversely, individuals with secure attachment styles exhibit minimal to moderate variations in relative energy, consistently maintaining a lower P200 energy level. Additionally, both secure and insecure individuals demonstrate heightened intensity in the P400 component in response to positive feedback. These findings underscore the influential role of attachment style in shaping emotional reactivity and regulation, emphasizing the significance of attachment theory in understanding individual differences in social and emotional functioning. This study provides novel insights into the neural mechanisms underlying the influence of attachment style on emotional processing within the context of cognitive task performance. Future research should consider diverse participant samples, employ objective measures of attachment, and utilize longitudinal designs to further explore the neural processes associated with attachment.

Applications of brain-computer interfaces in neurodegenerative diseases

Brain-computer interfaces (BCIs) provide the central nervous system with channels of direct communication to the outside world, without having to go through the peripheral nervous system. Neurodegenerative diseases (NDs) are notoriously incurable and burdensome medical conditions that will result in progressive deterioration of the nervous system. The applications of BCIs in NDs have been studied for decades now through different approaches, resulting in a considerable amount of literature in all related areas. In this study, we begin by introducing BCIs and proceed by explaining the principles of BCI-based neurorehabilitation. Then, we go through four specific types of NDs, including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and spinal muscular atrophy, and review some of the applications of BCIs in the neural rehabilitation of these diseases. We conclude with a discussion of the characteristics, challenges, and future possibilities of research in the field. Going through the uses of BCIs in NDs, we can see that approaches and strategies employed to tackle the wide range of limitations caused by NDs are numerous and diverse. Furthermore, NDs can fall under different categories based on the target area of neurodegeneration and thus require different methods of BCI-based rehabilitation. In recent years, neurotechnology companies have substantially invested in research on BCIs, focusing on commercializing BCIs and bringing BCI-based technologies from bench to bedside. This can mean the beginning of a new era for BCI-based neurorehabilitation, with an anticipated spike in interest among researchers, practitioners, engineers, and entrepreneurs alike.

Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition

Joint attention skills deficiency in Autism spectrum disorder (ASD) hinders individuals from communicating effectively. The P300 Electroencephalogram (EEG) signal-based brain-computer interface (BCI) helps these individuals in neurorehabilitation training to overcome this deficiency. The detection of the P300 signal is more challenging in ASD as it is noisy, has less amplitude, and has a higher latency than in other individuals. This paper presents a novel application of the variational mode decomposition (VMD) technique in a BCI system involving ASD subjects for P300 signal identification. The EEG signal is decomposed into five modes using VMD. Thirty linear and non-linear time and frequency domain features are extracted for each mode. Synthetic minority oversampling technique data augmentation is performed to overcome the class imbalance problem in the chosen dataset. Then, a comparative analysis of three popular machine learning classifiers is performed for this application. VMD's fifth mode with a support vector machine (fine Gaussian kernel) classifier gave the best performance parameters, namely accuracy, F1-score, and the area under the curve, as 91.12%, 91.18%, and 96.6%, respectively. These results are better when compared to other state-of-the-art methods.

The influential factors and non-pharmacological interventions of cognitive impairment in children with ischemic stroke

Background

The prevalence of pediatric ischemic stroke rose by 35% between 1990 and 2013. Affected patients can experience the gradual onset of cognitive impairment in the form of impaired language, memory, intelligence, attention, and processing speed, which affect 20-50% of these patients. Only few evidence-based treatments are available due to significant heterogeneity in age, pathological characteristics, and the combined epilepsy status of the affected children.

Methods

We searched the literature published by Web of Science, Scopus, and PubMed, which researched non-pharmacological rehabilitation interventions for cognitive impairment following pediatric ischemic stroke. The search period is from the establishment of the database to January 2022.

Results

The incidence of such impairment is influenced by patient age, pathological characteristics, combined epilepsy status, and environmental factors. Non-pharmacological treatments for cognitive impairment that have been explored to date mainly include exercise training, psychological intervention, neuromodulation strategies, computer-assisted cognitive training, brain-computer interfaces (BCI), virtual reality, music therapy, and acupuncture. In childhood stroke, the only interventions that can be retrieved are psychological intervention and neuromodulation strategies.

Conclusion

However, evidence regarding the efficacy of these interventions is relatively weak. In future studies, the active application of a variety of interventions to improve pediatric cognitive function will be necessary, and neuroimaging and electrophysiological measurement techniques will be of great value in this context. Larger multi-center prospective longitudinal studies are also required to offer more accurate evidence-based guidance for the treatment of patients with pediatric stroke.

A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders

Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.

Heart Rate Variability Biofeedback to Treat Anxiety in Young People With Autism Spectrum Disorder: Findings From a Home-Based Pilot Study

Background

People with autism spectrum disorder (ASD) frequently experience high levels of anxiety. Despite this, many clinical settings do not provide specialist ASD mental health services, and demand for professional support frequently outstrips supply. Across many sectors of health, investigators have explored digital health solutions to mitigate demand and extend the reach of professional practice beyond traditional clinical settings.

Objective

This critical appraisal and pilot feasibility study examines heart rate variability (HRV) biofeedback as an approach to help young people with ASD to manage anxiety symptoms outside of formal settings. The aim is to explore the use of portable biofeedback devices to manage anxiety, while also highlighting the risks and benefits of this approach with this population.

Methods

We assessed the feasibility of using home-based HRV biofeedback for self-management of anxiety in young people with ASD. We adopted coproduction, involving people with ASD, to facilitate development of the study design. Next, a separate pilot with 20 participants with ASD (n=16, 80% male participants and n=4, 20% female participants, aged 13-24 years; IQ>70) assessed adoption and acceptability of HRV biofeedback devices for home use over a 12-week period. Data were collected from both carers and participants through questionnaires and interviews; participants also provided single-lead electrocardiogram recordings as well as daily reports through smartphone on adoption and use of their device.

Results

Pre-post participant questionnaires indicated a significant reduction in anxiety in children (t₆=2.55; P=.04; Cohen d=0.99) as well as adults (t₇=3.95; P=.006; Cohen d=0.54). Participant age was significantly negatively correlated with all HRV variables at baseline, namely high-frequency heart rate variability (HF-HRV: P=.02), the root mean square of successive differences in normal heartbeat contractions (RMSSD: P=.02) and the variability of normal-to-normal interbeat intervals (SDNN: P=.04). At follow-up, only SDNN was significantly negatively correlated with age (P=.05). Levels of ASD symptoms were positively correlated with heart rate both before (P=.04) and after the intervention (P=.01). The majority (311/474, 65.6%) of reports from participants indicated that the devices helped when used. Difficulties with the use of some devices and problems with home testing of HRV were noted. These initial findings are discussed within the context of the strengths and challenges of remotely delivering a biofeedback intervention for people with ASD.

Conclusions

HRV biofeedback devices have shown promise in this pilot study. There is now a need for larger evaluation of biofeedback to determine which delivery methods achieve the greatest effect for people with ASD.

Trial Registration

ClinicalTrials.gov NCT04955093; https://clinicaltrials.gov/ct2/show/NCT04955093

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.

Evaluating commercially available wireless cardiovascular monitors for measuring and transmitting real-time physiological responses in children with autism

Commercially available wearable biosensors have the potential to enhance psychophysiology research and digital health technologies for autism by enabling stress or arousal monitoring in naturalistic settings. However, such monitors may not be comfortable for children with autism due to sensory sensitivities. To determine the feasibility of wearable technology in children with autism age 8-12 years, we first selected six consumer-grade wireless cardiovascular monitors and tested them during rest and movement conditions in 23 typically developing adults. Subsequently, the best performing monitors (based on data quality robustness statistics), Polar and Mio Fuse, were evaluated in 32 children with autism and 23 typically developing children during a 2-h session, including rest and mild stress-inducing tasks. Cardiovascular data were recorded simultaneously across monitors using custom software. We administered the Comfort Rating Scales to children. Although the Polar monitor was less comfortable for children with autism than typically developing children, absolute scores demonstrated that, on average, all children found each monitor comfortable. For most children, data from the Mio Fuse (96%-100%) and Polar (83%-96%) passed quality thresholds of data robustness. Moreover, in the stress relative to rest condition, heart rate increased for the Polar, F(1,53) = 135.70, p < 0.001, ηp²  = 0.78, and Mio Fuse, F(1,53) = 71.98, p < 0.001, ηp²  = 0.61, respectively, and heart rate variability decreased for the Polar, F(1,53) = 13.41, p = 0.001, ηp²  = 0.26, and Mio Fuse, F(1,53) = 8.89, p = 0.005, ηp²  = 0.16, respectively. This feasibility study suggests that select consumer-grade wearable cardiovascular monitors can be used with children with autism and may be a promising means for tracking physiological stress or arousal responses in community settings. LAY SUMMARY: Commercially available heart rate trackers have the potential to advance stress research with individuals with autism. Due to sensory sensitivities common in autism, their comfort wearing such trackers is vital to gathering robust and valid data. After assessing six trackers with typically developing adults, we tested the best trackers (based on data quality) in typically developing children and children with autism and found that two of them met criteria for comfort, robustness, and validity.

Modification of Joint Recurrence Quantification Analysis (JRQA) for assessing individual characteristics from short EEG time series

This article proposes a modification of joint recurrence quantification analysis for identifying individual characteristics applied to human electroencephalography (EEG) using short time series. Statistical analysis of EEG characteristics facilitated the clarification of the spatial localization of identified individual characteristics. The method can be adapted for use as a stage of a rapid automatic configuration of brain-computer interface devices, which is especially relevant when working with children, due to limited opportunities for their long-term monitoring.

Applications of Biological and Physiological Signals in Commercial Video Gaming and Game Research: A Review

Video gaming is now available as a fully immersive experience that creates responsive inputs and outputs concerning the user, and some experimental developers have integrated the use of the voice, brain, or muscles as input controls. The use of physiological signal equipment can provide valuable information regarding the emotion of a player or patient during gameplay. In this article, we discuss five of the most common biosignals that are used in gaming research, and their function and devices that may be used for measurement. We break down those individual signals and present examples of research studies that implement them. We also discuss the usage of biological signals within commercial gaming and conclude with some possible future directions for the use of biological signals in gaming and game research.

Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review

The aim of this article is to explore a paradigm shift on Brain Computer Interface (BCI) research, as well as on intervention best practices for training and rehabilitation of students with neurodevelopmental disorders. Recent studies indicate that BCI devices have positive impact on students' attention skills and working memory as well as on other skills, such as visuospatial, social, imaginative and emotional abilities. BCI applications aim to emulate humans' brain and address the appropriate understanding for each student's neurodevelopmental disorders. Studies conducted to provide knowledge about BCI-based intervention applications regarding memory, attention, visuospatial, learning, collaboration, and communication, social, creative and emotional skills are highlighted. Only non-invasive BCI type of applications are being investigated based upon representative, non-exhaustive and state-of-the-art studies within the field. This article examines the progress of BCI research so far, while different BCI paradigms are investigated. BCI-based applications could successfully regulate students' cognitive abilities when used for their training and rehabilitation. Future directions to investigate BCI-based applications for training and rehabilitation of students with neurodevelopmental disorders concerning the different populations involved are discussed.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations

The brain is the most important organ of the human body, and the conversations between the brain and an apparatus can not only reveal a normally functioning or a dysfunctional brain but also can modulate the brain. Here, the apparatus may be a nonbiological instrument, such as a computer, and the consequent brain-computer interface is now a very popular research area with various applications. The apparatus may also be a biological organ or system, such as the gut and muscle, and their efficient conversations with the brain are vital for a healthy life. Are there any common bases that bind these different scenarios? Here, we propose a new comprehensive cross area: Bacomics, which comes from brain-apparatus conversations (BAC) + omics. We take Bacomics to cover at least three situations: (1) The brain is normal, but the conversation channel is disabled, as in amyotrophic lateral sclerosis. The task is to reconstruct or open up new channels to reactivate the brain function. (2) The brain is in disorder, such as in Parkinson's disease, and the work is to utilize existing or open up new channels to intervene, repair and modulate the brain by medications or stimulation. (3) Both the brain and channels are in order, and the goal is to enhance coordinated development between the brain and apparatus. In this paper, we elaborate the connotation of BAC into three aspects according to the information flow: the issue of output to the outside (BAC-1), the issue of input to the brain (BAC-2) and the issue of unity of brain and apparatus (BAC-3). More importantly, there are no less than five principles that may be taken as the cornerstones of Bacomics, such as feedforward and feedback control, brain plasticity, harmony, the unity of opposites and systems principles. Clearly, Bacomics integrates these seemingly disparate domains, but more importantly, opens a much wider door for the research and development of the brain, and the principles further provide the general framework in which to realize or optimize these various conversations.

Video games as rich environments to foster brain plasticity

This chapter highlights the key role of two main factors, attentional control and reward processing, in unlocking brain plasticity. We first review the evidence for the role that each of these mechanisms plays in neuroplasticity, and then make the case that tools and technologies that combine these two are likely to result in maximal and broad, generalized benefits. In this context, we review the evidence concerning the impact of video game play on brain plasticity, with an eye toward plasticity-driving methods such as the seamless integration of neurofeedback into the video game platforms.

State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review

Brain–computer interface (BCI) is a novel communication method between brain and machine. It enables signals from the human brain to influence or control external devices. Currently, much research interest is focused on the BCI-based neural rehabilitation of patients with motor and cognitive diseases. Over the decades, BCI has become an alternative treatment for motor and cognitive rehabilitation. Previous studies demonstrated the usefulness of BCI intervention in restoring motor function and recovery of the damaged brain. Electroencephalogram (EEG)-based BCI intervention could cast light on the mechanisms underlying neuroplasticity during upper limb recovery by providing feedback to the damaged brain. BCI could act as a useful tool to aid patients with daily communication and basic movement in severe motor loss cases like amyotrophic lateral sclerosis (ALS). Furthermore, recent findings have reported the therapeutic efficacy of BCI in people suffering from other diseases with different levels of motor impairment such as spastic cerebral palsy, neuropathic pain, etc. Besides motor functional recovery, BCI also plays its role in improving the behavior of patients with cognitive diseases like attention-deficit/hyperactivity disorder (ADHD). The BCI-based neurofeedback training is focused on either reducing the ratio of theta and beta rhythm, or enabling the patients to regulate their own slow cortical potentials, and both have made progress in increasing attention and alertness. With summary of several clinical studies with strong evidence, we present cutting edge results from the clinical application of BCI in motor and cognitive diseases, including stroke, spinal cord injury, ALS, and ADHD.

Diagnostic Assessment Techniques and Non-Invasive Biomarkers for Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex heterogeneous neurological disorder that has led to a spectrum of diagnosis techniques. The screening instruments, medical and technological tools initiate the diagnosis process. Clinicians and psychologists propose therapies depending on the examination done by these methodologies. The literature has accounted dozens of diagnostic methods and alternative and complementary therapies but still lack in highlighting the proper biomarker for early detection and intervention. The emerging multi-modal neuro-imaging techniques have correlated the brain's functional and structural measures and diagnosed ASD with more sensitivity than individual approaches. The purpose of this review article is: (i) to provide an overview of the emerging ASD diagnosis methods and different markers and; (ii) to present the idea of integrating all the individual methods in to a multi-modal diagnostic system to enhance detection sensitivity. This system possesses the potential to diagnose and predict ASD clinically, neurologically & objectively with high detection sensitivity.

Neurobiological Aspects of Face Recognition: The Role of Oxytocin

Face recognition is an important index in the formation of social cognition and neurodevelopment in humans. Changes in face perception and memory are connected with altered sociability, which is a symptom of numerous brain conditions including autism spectrum disorder (ASD). Various brain regions and neuropeptides are implicated in face processing. The neuropeptide oxytocin (OT) plays an important role in various social behaviors, including face and emotion recognition. Nasal OT administration is a promising new therapy that can address social cognition deficits in individuals with ASD. New instrumental neurotechnologies enable the assessment of brain region activation during specific social tasks and therapies, and can characterize the involvement of genes and peptides in impaired neurodevelopment. The present review sought to discuss some of the mechanisms of the face distinguishing process, the ability of OT to modulate social cognition, as well as new perspectives and technologies for research and rehabilitation of face recognition.

A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface

Deficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibility clinical trial and enrolled 15 participants (mean age 22y 2m) with high-functioning ASD (mean full-scale IQ 103). Participants were submitted to a BCI training paradigm using a virtual reality interface over seven sessions spread over 4 months. The first four sessions occurred weekly, and the remainder monthly. In each session, the subject was asked to identify objects of interest based on the gaze direction of an avatar. Attentional responses were extracted from the EEG P300 component. A final follow-up assessment was performed 6-months after the last session. To analyze responses to joint attention cues participants were assessed pre and post intervention and in the follow-up, using an ecologic “Joint-attention task.” We used eye-tracking to identify the number of social attention items that a patient could accurately identify from an avatar's action cues (e.g., looking, pointing at). As secondary outcome measures we used the Autism Treatment Evaluation Checklist (ATEC) and the Vineland Adaptive Behavior Scale (VABS). Neuropsychological measures related to mood and depression were also assessed. In sum, we observed a decrease in total ATEC and rated autism symptoms (Sociability; Sensory/Cognitive Awareness; Health/Physical/Behavior); an evident improvement in Adapted Behavior Composite and in the DLS subarea from VABS; a decrease in Depression (from POMS) and in mood disturbance/depression (BDI). BCI online performance and tolerance were stable along the intervention. Average P300 amplitude and alpha power were also preserved across sessions. We have demonstrated the feasibility of BCI in this kind of intervention in ASD. Participants engage successfully and consistently in the task. Although the primary outcome (rate of automatic responses to joint attention cues) did not show changes, most secondary neuropsychological outcome measures showed improvement, yielding promise for a future efficacy trial.

(clinical-trial ID: NCT02445625—clinicaltrials.gov).

The Treatment of Autism Spectrum Disorder With Auditory Neurofeedback: A Randomized Placebo Controlled Trial Using the Mente Autism Device

Introduction: Children affected by autism spectrum disorder (ASD) often have impairment of social interaction and demonstrate difficulty with emotional communication, display of posture and facial expression, with recognized relationships between postural control mechanisms and cognitive functions. Beside standard biomedical interventions and psychopharmacological treatments, there is increasing interest in the use of alternative non-invasive treatments such as neurofeedback (NFB) that could potentially modulate brain activity resulting in behavioral modification.

Methods: Eighty-three ASD subjects were randomized to an Active group receiving NFB using the Mente device and a Control group using a Sham device. Both groups used the device each morning for 45 minutes over a 12 week home based trial without any other clinical interventions. Pre and Post standard ASD questionnaires, qEEG and posturography were used to measure the effectiveness of the treatment.

Results: Thirty-four subjects (17 Active and 17 Control) completed the study. Statistically and substantively significant changes were found in several outcome measures for subjects that received the treatment. Similar changes were not detected in the Control group.

Conclusions: Our results show that a short 12 week course of NFB using the Mente Autism device can lead to significant changes in brain activity (qEEG), sensorimotor behavior (posturography), and behavior (standardized questionnaires) in ASD children.

Facilitating Neurofeedback in Children with Autism and Intellectual Impairments Using TAGteach

Individuals with autism and intellectual impairments tend to be excluded from research due to their difficulties with methodological compliance. This study focuses on using Teaching with Acoustic Guidance-TAGteach-to behaviorally prepare children with autism and a IQ ≤ 80 to participate in a study on neurofeedback training (NFT). Seven children (ages 6-8) learned the prerequisite skills identified in a task analysis in an average of 5 h of TAGteach training, indicating that this is a feasible method of preparing intellectually-impaired children with autism to participate in NFT and task-dependent electroencephalography measures. TAGteach may thus have the potential to augment this population's ability to participate in less accessible treatments and behavioral neuroscientific studies.

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.

Effectiveness Evaluation of Real-Time Scalp Signal Separating Algorithm on Near-Infrared Spectroscopy Neurofeedback

Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied. To demonstrate its effectiveness, two separate neurofeedback experiments were conducted. In the first experiment, the feedback signal was the raw NIRS signal recorded while in the second experiment, deep signal extracted using RT-SSS algorithm was used as the feedback signal. In both experiments, participants were instructed to control the feedback signal to follow a predefined track. Accuracy scores were calculated based on the differences between the trace controlled by feedback signal and the targeted track. Overall, the second experiment yielded better performance in terms of accuracy scores. These findings proved that RT-SSS algorithm is beneficial for neurofeedback.

Separability of motor imagery of the self from interpretation of motor intentions of others at the single trial level: an EEG study

BACKGROUND

We aimed to investigate the separability of the neural correlates of 2 types of motor imagery, self and third person (actions owned by the participant himself vs. another individual). If possible this would allow for the development of BCI interfaces to train disorders of action and intention understanding beyond simple imitation, such as autism.

METHODS

We used EEG recordings from 20 healthy participants, as well as electrocorticography (ECoG) in one, based on a virtual reality setup. To test feasibility of discrimination between each type of imagery at the single trial level, time-frequency and source analysis were performed and further assessed by data-driven statistical classification using Support Vector Machines.

RESULTS

The main observed differences between self-other imagery conditions in topographic maps were found in Frontal and Parieto-Occipital regions, in agreement with the presence of 2 independent non μ related contributions in the low alpha frequency range. ECOG corroborated such separability. Source analysis also showed differences near the temporo-parietal junction and single-trial average classification accuracy between both types of motor imagery was 67 ± 1%, and raised above 70% when 3 trials were used. The single-trial classification accuracy was significantly above chance level for all the participants of this study (p < 0.02).

CONCLUSIONS

The observed pattern of results show that Self and Third Person MI use distinct electrophysiological mechanisms detectable at the scalp (and ECOG) at the single trial level, with separable levels of involvement of the mirror neuron system in different regions. These observations provide a promising step to develop new BCI training/rehabilitation paradigms for patients with neurodevelopmental disorders of action understanding beyond simple imitation, such as autism, who would benefit from training and anticipation of the perceived intention of others as opposed to own intentions in social contexts.

Frontal Alpha Oscillations and Attentional Control: A Virtual Reality Neurofeedback Study

Two competing views about alpha oscillations suggest that cortical alpha reflect either cortical inactivity or cortical processing efficiency. We investigated the role of alpha oscillations in attentional control, as measured with a Stroop task. We used neurofeedback to train 22 participants to increase their level of alpha amplitude. Based on the conflict/control loop theory, we selected to train prefrontal alpha and focus on the Gratton effect as an index of deployment of attentional control. We expected an increase or a decrease in the Gratton effect with increase in neural learning depending on whether frontal alpha oscillations reflect cortical idling or enhanced processing efficiency, respectively. In order to induce variability in neural learning beyond natural occurring individual differences, we provided half of the participants with feedback on alpha amplitude in a 3-dimensional (3D) virtual reality environment and the other half received feedback in a 2D environment. Our results showed variable neural learning rates, with larger rates in the 3D compared to the 2D group, corroborating prior evidence of individual differences in EEG-based learning and the influence of a virtual environment. Regression analyses revealed a significant association between the learning rate and changes on deployment of attentional control, with larger learning rates being associated with larger decreases in the Gratton effect. This association was not modulated by feedback medium. The study supports the view of frontal alpha oscillations being associated with efficient neurocognitive processing and demonstrates the utility of neurofeedback training in addressing theoretical questions in the non-neurofeedback literature.

Optical-imaging-based neurofeedback to enhance therapeutic intervention in adolescents with autism: methodology and initial data

Impaired facial processing may contribute to social dysfunction in certain individuals with autism spectrum disorder (ASD). Prior studies show that electroencephalogram-based and functional magnetic resonance imaging-based neurofeedback might help some individuals with ASD learn to modulate regional brain activity and thus reduce symptoms. Here, we report for the first time the feasibility of employing functional near-infrared spectroscopy (fNIRS)-based neurofeedback training in children with ASD. We developed a method to study physiological self-regulation of oxy-hemoglobin using real-time feedback. The paradigm is illustrated with initial data from four subjects who engaged in a facial-identity recognition training program during which an implicit reinforcement was given based on the participant's brain activity and behavioral performance. Two participants had a confirmed diagnosis of ASD, and the other two were typically developing (TD). One participant with ASD and one TD participant received real-feedback (real-FB) during the training, whereas the other two received sham-feedback (sham-FB). After five training sessions, the subjects who received real-FB showed more improvement in facial recognition performance compared with those receiving sham-FB, particularly in the participant with ASD. These results suggest fNIRS-based neurofeedback could enhance therapeutic intervention in children with ASD.

An Effective Neurofeedback Intervention to Improve Social Interactions in Children with Autism Spectrum Disorder

Neurofeedback training (NFT) approaches were investigated to improve behavior, cognition and emotion regulation in children with autism spectrum disorder (ASD). Thirteen children with ASD completed pre-/post-assessments and 16 NFT-sessions. The NFT was based on a game that encouraged social interactions and provided feedback based on imitation and emotional responsiveness. Bidirectional training of EEG mu suppression and enhancement (8-12 Hz over somatosensory cortex) was compared to the standard method of enhancing mu. Children learned to control mu rhythm with both methods and showed improvements in (1) electrophysiology: increased mu suppression, (2) emotional responsiveness: improved emotion recognition and spontaneous imitation, and (3) behavior: significantly better behavior in every-day life. Thus, these NFT paradigms improve aspects of behavior necessary for successful social interactions.

Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits

When the sensory-motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain-computer interface (BCI) is a tool that permits to reintegrate the sensory-motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system.

Behavioral disturbance and treatment strategies in Smith-Magenis syndrome

Background

Smith-Magenis syndrome is a complex neurodevelopmental disorder that includes intellectual deficiency, speech delay, behavioral disturbance and typical sleep disorders. Ninety percent of the cases are due to a 17p11.2 deletion encompassing the RAI1 gene; other cases are linked to mutations of the same gene. Behavioral disorders often include outbursts, attention deficit/hyperactivity disorders, self-injury with onychotillomania and polyembolokoilamania (insertion of objects into body orifices), etc. Interestingly, the stronger the speech delay and sleep disorders, the more severe the behavioral issues. Sleep disturbances associate excessive daytime sleepiness with nighttime agitation. They are underpinned by an inversion of the melatonin secretion cycle. However, the combined intake of beta-blockers in the morning and melatonin in the evening may radically alleviate the circadian rhythm problems.

Discussion

Once sleep disorders are treated, the next challenge is finding an effective treatment for the remaining behavioral problems. Unfortunately, there is a lack of objective guidelines. A comprehensive evaluation of such disorders should include sleep disorders, potential causes of pain, neurocognitive level and environment (i.e. family and school). In any case, efforts should focus on improving communication skills, identifying and treating attention deficit/hyperactivity, aggressiveness and anxiety.

Summary

Treatment of Smith-Magenis syndrome is complex and requires a multidisciplinary team including, among others, geneticists, psychiatrists, neuropediatricians/neurologists, somnologists, developmental and behavioral pediatricians, and speech and language therapists.

The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces

Individuals with severe neuromuscular impairments face many challenges in communication and manipulation of the environment. Brain-computer interfaces (BCIs) show promise in presenting real-world applications that can provide such individuals with the means to interact with the world using only brain waves. Although there has been a growing body of research in recent years, much relates only to technology, and not to technology in use-i.e., real-world assistive technology employed by users. This review examined the literature to highlight studies that implicate the human factors and ergonomics (HFE) of P300-based BCIs. We assessed 21 studies on three topics to speak directly to improving the HFE of these systems: (1) alternative signal evocation methods within the oddball paradigm; (2) environmental interventions to improve user performance and satisfaction within the constraints of current BCI systems; and (3) measures and methods of measuring user acceptance. We found that HFE is central to the performance of P300-based BCI systems, although researchers do not often make explicit this connection. Incorporation of measures of user acceptance and rigorous usability evaluations, increased engagement of disabled users as test participants, and greater realism in testing will help progress the advancement of P300-based BCI systems in assistive applications.

Conceptual issues in autism spectrum disorders

PURPOSE OF REVIEW

To provide an update on recent studies concerning social cognition in autism spectrum disorders (ASDs), to compare different theoretical approaches used to interpret empirical data, and to highlight a number of conceptual issues.

RECENT FINDINGS

In regard to social cognition in ASDs, there is an emerging emphasis on early-onset and prolonged sensory-motor problems. Such sensory-motor problems may fit with the theories of social cognition that emphasize the importance of embodied interaction rather than deficits in mindreading, or they may reflect more general aspects of developmental disorders.

SUMMARY

Different theoretical frameworks offer alternative perspectives on the central characteristics in ASDs and motivate different ways of conceptualizing diagnosis and intervention. Theory-of-mind approaches continue to appeal to false-belief paradigms, and debate continues about the performance of individuals with autism. Likewise, there is continuing debate and renewed skepticism about the role of simulation and deficits in the mirror system in ASDs. Growing evidence concerning sensory-motor problems, specifically disrupted patterns in re-entrant (afferent and proprioceptive) sensory feedback across the autistic spectrum, may not only provide support for more embodied interactive approaches, but also suggests that a single approach is unlikely able to explain all social cognition problems in autism. A pluralist approach understands ASDs as involving a variant range of cascading disrupted processes.