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Browsing by Author "Jaime, Mark"
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Item Brief Report: Reduced Temporal-Central EEG Alpha Coherence During Joint Attention Perception in Adolescents with Autism Spectrum Disorder(Springer, 2016-04) Jaime, Mark; McMahon, Camilla M.; Davidson, Bridget C.; Newell, Lisa C.; Mundy, Peter C.; Henderson, Heather A.; Science, IUPUI ColumbusAlthough prior studies have demonstrated reduced resting state EEG coherence in adults with autism spectrum disorder (ASD), no studies have explored the nature of EEG coherence during joint attention. We examined the EEG coherence of the joint attention network in adolescents with and without ASD during congruent and incongruent joint attention perception and an eyes-open resting condition. Across conditions, adolescents with ASD showed reduced right hemisphere temporal-central alpha coherence compared to typically developing adolescents. Greater right temporal-central alpha coherence during joint attention was positively associated with social cognitive performance in typical development but not in ASD. These results suggest that, in addition to a resting state, EEG coherence during joint attention perception is reduced in ASD.Item An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder(IEEE, 2019) Haputhanthri, Dilantha; Brihadiswaran, Gunavaran; Gunathilaka, Sahan; Meedeniya, Dulani; Jayawardena, Yasith; Jayarathna, Sampath; Jaime, Mark; IUPUC Division of ScienceAutism Spectrum Disorder (ASD) is a neurodevelopmental condition which affects a person's cognition and behaviour. It is a lifelong condition which cannot be cured completely using any intervention to date. However, early diagnosis and follow-up treatments have a major impact on autistic people. Unfortunately, the current diagnostic practices, which are subjective and behaviour dependent, delay the diagnosis at an early age and makes it harder to distinguish autism from other developmental disorders. Several works of literature explore the possible behaviour-independent measures to diagnose ASD. Abnormalities in EEG can be used as reliable biomarkers to diagnose ASD. This work presents a low-cost and straightforward diagnostic approach to classify ASD based on EEG signal processing and learning models. Possibilities to use a minimum number of EEG channels have been explored. Statistical features are extracted from noise filtered EEG data before and after Discrete Wavelet Transform. Relevant features and EEG channels were selected using correlation-based feature selection. Several learning models and feature vectors have been studied and possibilities to use the minimum number of EEG channels have also been explored. Using Random Forest and Correlation-based Feature Selection, an accuracy level of 93% was obtained.Item Metacognitive Awareness of Facial Affect in Higher-Functioning Children and Adolescents with Autism Spectrum Disorder(Springer, 2016-03) McMahon, Camilla M.; Henderson, Heather A.; Newell, Lisa; Jaime, Mark; Mundy, Peter; Department of Psychology, School of ScienceHigher-functioning participants with and without autism spectrum disorder (ASD) viewed a series of face stimuli, made decisions regarding the affect of each face, and indicated their confidence in each decision. Confidence significantly predicted accuracy across all participants, but this relation was stronger for participants with typical development than participants with ASD. In the hierarchical linear modeling analysis, there were no differences in face processing accuracy between participants with and without ASD, but participants with ASD were more confident in their decisions. These results suggest that individuals with ASD have metacognitive impairments and are overconfident in face processing. Additionally, greater metacognitive awareness was predictive of better face processing accuracy, suggesting that metacognition may be a pivotal skill to teach in interventions.