Resting state electroencephalogram in autism spectrum disorder identification based on neuro-physiological interface of affect (NPIA) modelling

Children with autism spectrum disorder (ASD) is likely to have repetitive and restricted repertoire in its behaviors, activities and interests. Early detection and intervention of ASD can help these children to lead an almost normal life. Thus it is important to ensure that early detection of such ASD preschoolers can be carried out. The brain connectivity of ASD can be achieved better by capturing and analyzing through the EEG and machine learning. In this paper we presented both the time domain approach, which were used by most researchers to identify ASD and also the neuro-physiological interface of affect (NPIA) at resting state. There seems to be consistency in results based on the NPIA at resting state for eyes opened and eyes closed while using time domain approach shows otherwise. Therefore, both models can be used to have a better accuracy in diagnosing an ASD. Future works also can have the NPIA model approaches on the other learning disabilities

Automating autism: Disability, discourse, and Artificial Intelligence

As Artificial Intelligence (AI) systems shift to interact with new domains and populations, so does AI ethics: a relatively nascent subdiscipline that frequently concerns itself with questions of “fairness” and “accountability.” This fairness-centred approach has been criticized for (amongst other things) lacking the ability to address discursive, rather than distributional, injustices. In this paper I simultaneously validate these concerns, and work to correct the relative silence of both conventional and critical AI ethicists around disability, by exploring the narratives deployed by AI researchers in discussing and designing systems around autism. Demonstrating that these narratives frequently perpetuate a dangerously dehumanizing model of autistic people, I explore the material consequences this might have. More importantly, I highlight the ways in which discursive harms—particularly discursive harms around dehumanization—are not simply inadequately handled by conventional AI ethics approaches, but actively invisible to them. I urge AI ethicists to critically and immediately begin grappling with the likely consequences of an approach to ethics which focuses on personhood and agency, in a world in which many populations are treated as having neither. I suggest that this issue requires a substantial revisiting of the underlying premises of AI ethics, and point to some possible directions in which researchers and practitioners might look for inspiration

EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents a survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning. The methodology is divided into four phases: EEG data collection, pre-processing, feature extraction and classification. This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model. We analyze the strengths and weaknesses of the techniques and tools. Further, this study summarizes the ASD classification approaches and discusses the existing challenges, limitations and future directions

Social attention in young children with autism spectrum disorder: Investigating cross-contextual gaze behaviours, and their relationship to autism severity, cognitive skills and social functioning

Social communication and interaction challenges are characteristic of autism spectrum disorder (ASD). Social attention has emerged to be an important behavioural phenotype in ASD, with accumulating evidence suggesting associations with social functioning and developmental outcomes. However, research gaps remain concerning the nature of social attention, the variability demonstrated across different experimental tasks and social contexts, and the ecologically validity of research methods. This thesis aimed to address these substantive and methodological issues by examining social attention patterns in a young cohort of autistic children, and their age-matched neurotypical peers, across three experimental contexts: 1) a traditional, eye-tracking task with static stimuli, 2) a novel, dynamic eye-tracking task incorporating shared book reading (SBR), and 3) an evaluation of the association in social attention across the two eye-tracking tasks and a play-based social interaction task. In Chapter 2, the influence of circumscribed interests (CI) on social attention patterns was investigated. The results of this study suggested there to be a reduced role for CIs and atypical attention patterns in both social and non-social domains. In Chapter 3, a novel SBR task was developed as a dynamic, ecologically relevant eye-tracking task designed to assess social and joint attention behaviours. Results indicated reduced social and joint attention behaviours, in conjunction with increased attention to non-salient background objects in autistic children. Associations between reduced social attention and poorer social functioning and cognitive skills were also evident in this cohort. In Chapter 4, the social attention patterns of the autistic cohort as measured by the two previous eye tracking tasks were correlated with these patterns in a live, play-based social interaction task between a researcher and the autistic child. Cross-contextual associations in social attention between the social interaction and dynamic tasks, and the dynamic and static tasks were observed. In contrast, there was no significant association in social attention patterns between the social interaction and static tasks. These outcomes contribute new insights into the social attention behaviours of autistic children, and evidence in favor of examining these behaviours in ecologically relevant contexts. They also contribute to evidence associating social attention with autism symptomatology and cognitive functioning. Ultimately, the outcomes of this research may improve our understanding of the needs of autistic children across social, cognitive and adaptive functioning domains