Recent Trends in Non-invasive Neural Recording Based Brain-to-Brain Synchrony Analysis on Multidisciplinary Human Interactions for Understanding Brain Dynamics: A Systematic Review.

The study of brain-to-brain synchrony has a burgeoning application in the brain-computer interface (BCI) research, offering valuable insights into the neural underpinnings of interacting human brains using numerous neural recording technologies. The area allows exploring the commonality of brain dynamics by evaluating the neural synchronization among a group of people performing a specified task. The growing number of publications on brain-to-brain synchrony inspired the authors to conduct a systematic review using the PRISMA protocol so that future researchers can get a comprehensive understanding of the paradigms, methodologies, translational algorithms, and challenges in the area of brain-to-brain synchrony research. This review has gone through a systematic search with a specified search string and selected some articles based on pre-specified eligibility criteria. The findings from the review revealed that most of the articles have followed the social psychology paradigm, while 36% of the selected studies have an application in cognitive neuroscience. The most applied approach to determine neural connectivity is a coherence measure utilizing phase-locking value (PLV) in the EEG studies, followed by wavelet transform coherence (WTC) in all of the fNIRS studies. While most of the experiments have control experiments as a part of their setup, a small number implemented algorithmic control, and only one study had interventional or a stimulus-induced control experiment to limit spurious synchronization. Hence, to the best of the authors' knowledge, this systematic review solely contributes to critically evaluating the scopes and technological advances of brain-to-brain synchrony to allow this discipline to produce more effective research outcomes in the remote future.

Autonomic and Electrophysiological Evidence for Reduced Auditory Habituation in Autism.

It is estimated that nearly 90% of children on the autism spectrum exhibit sensory atypicalities. What aspects of sensory processing are affected in autism? Although sensory processing can be studied along multiple dimensions, two of the most basic ones involve examining instantaneous sensory responses and how the responses change over time. These correspond to the dimensions of 'sensitivity' and 'habituation'. Results thus far have indicated that autistic individuals do not differ systematically from controls in sensory acuity/sensitivity. However, data from studies of habituation have been equivocal. We have studied habituation in autism using two measures: galvanic skin response (GSR) and magneto-encephalography (MEG). We report data from two independent studies. The first study, was conducted with 13 autistic and 13 age-matched neurotypical young adults and used GSR to assess response to an extended metronomic sequence. The second study involved 24 participants (12 with an ASD diagnosis), different from those in study 1, spanning the pre-adolescent to young adult age range, and used MEG. Both studies reveal consistent patterns of reduced habituation in autistic participants. These results suggest that autism, through mechanisms that are yet to be elucidated, compromises a fundamental aspect of sensory processing, at least in the auditory domain. We discuss the implications for understanding sensory hypersensitivities, a hallmark phenotypic feature of autism, recently proposed theoretical accounts, and potential relevance for early detection of risk for autism.

Reduced Sensory Habituation in Autism and Its Correlation with Behavioral Measures.

Autism is strongly associated with sensory processing difficulties. We investigate sensory habituation, given its relevance for understanding important phenotypic traits like hyper- and hypo-sensitivities. We collected electroencephalography data from 22 neuro-typical(NT) and 13 autistic(ASD) children during the presentation of visual and auditory sequences of repeated stimuli. Our data show that the ASD children have significantly reduced habituation relative to the NT children for both auditory and visual stimuli. These results point to impaired habituation as a modality-general phenomenon in ASD. Additionally, the rates of habituation are correlated with several clinical scores associated with competence along diverse phenotypic dimensions. These data suggest that the sensory difficulties in autism are likely to be associated with reduced habituation and are related to clinical symptomology.

GOLIAH: A Gaming Platform for Home-Based Intervention in Autism - Principles and Design.

Children with Autism need intensive intervention and this is challenging in terms of manpower, costs, and time. Advances in Information Communication Technology and computer gaming may help in this respect by creating a nomadically deployable closed-loop intervention system involving the child and active participation of parents and therapists. An automated serious gaming platform enabling intensive intervention in nomadic settings has been developed by mapping two pivotal skills in autism spectrum disorder: Imitation and Joint Attention (JA). Eleven games - seven Imitations and four JA - were derived from the Early Start Denver Model. The games involved application of visual and audio stimuli with multiple difficulty levels and a wide variety of tasks and actions pertaining to the Imitation and JA. The platform runs on mobile devices and allows the therapist to (1) characterize the child's initial difficulties/strengths, ensuring tailored and adapted intervention by choosing appropriate games and (2) investigate and track the temporal evolution of the child's progress through a set of automatically extracted quantitative performance metrics. The platform allows the therapist to change the game or its difficulty levels during the intervention depending on the child's progress. Performance of the platform was assessed in a 3-month open trial with 10 children with autism (Trial ID: NCT02560415, Clinicaltrials.gov). The children and the parents participated in 80% of the sessions both at home (77.5%) and at the hospital (90%). All children went through all the games but, given the diversity of the games and the heterogeneity of children profiles and abilities, for a given game the number of sessions dedicated to the game varied and could be tailored through automatic scoring. Parents (N = 10) highlighted enhancement in the child's concentration, flexibility, and self-esteem in 78, 89, and 44% of the cases, respectively, and 56% observed an enhanced parents-child relationship. This pilot study shows the feasibility of using the developed gaming platform for home-based intensive intervention. However, the overall capability of the platform in delivering intervention needs to be assessed in a bigger open trial.

Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG.

BACKGROUND: Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. NEW METHOD: In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. RESULTS: Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR)-an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. COMPARISON WITH EXISTING METHOD(S): Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. CONCLUSIONS: Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts.

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates.

OBJECTIVE: The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. APPROACH: Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. MAIN RESULTS: The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. SIGNIFICANCE: The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

Existence of millisecond-order stable states in time-varying phase synchronization measure in EEG signals.

In this paper, we have developed a new measure of understanding the temporal evolution of phase synchronization for EEG signals using cross-electrode information. From this measure it is found that there exists a small number of well-defined phase-synchronized states, each of which is stable for few milliseconds during the execution of a face perception task. We termed these quasi-stable states as synchrostates. We used k-means clustering algorithms to estimate the optimal number of synchrostates from 100 trials of EEG signals over 128 channels. Our results show that these synchrostates exist consistently in all the different trials. It is also found that from the onset of the stimulus, switching between these synchrostates results in well-behaved temporal sequence with repeatability which may be indicative of the dynamics of the cognitive process underlying that task. Therefore these synchrostates and their temporal switching sequences may be used as a new measure of the stability of phase synchrony and information exchange between different regions of a human brain.