Fusiform Gyrus responses to neutral and emotional faces in children with Autism Spectrum Disorders: a High Density ERP study

Apicella, Fabio; Sicca, Federico; Federico, Rosario R.; Campatelli, Giulia; Muratori, Filippo

doi:10.1016/j.bbr.2012.10.040

Cited by 57 publications

(74 citation statements)

References 38 publications

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“…Other medical cases include autism [6,21,75,140,212,213], bipolar disorder [124,145], epilepsy [230,234,235], attention-deficit/hyperactivity disorder [103,165], bulimia nervosa [33,168], borderline personality disorder [167], pervasive developmental disorder [81], and eye movement desensitization and reprocessing [125].…”

Section: Othermentioning

confidence: 99%

“…Moreover, different age groups ranging from infants [132,143,169], children [6,43,75,84,89,103,125,126,[128][129][130]140,183,184,[210][211][212][213], adolescents [59,65,70,135,139], and elderly [105,214] in different experiments were considered as study samples. Some studies were conducted on a single gender, such as those investigating women's emotions during motherhood and parental status [133,134,164] and a study of men [215].…”

Section: Mahnobmentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Othermentioning

confidence: 99%

Section: Mahnobmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…The experimental population for this study consists of 12 healthy control children with neuro-typical development and 11 children diagnosed as ASD -both groups in the age range of 6 -13 years [9]. Both groups were presented three types of emotional face stimuli with standardized face expressions of happiness, fear and neutral expression which were presented 40 times [9].…”

Section: B Experimental Paradigm With Typical and Asd Childrenmentioning

confidence: 99%

“…Both groups were presented three types of emotional face stimuli with standardized face expressions of happiness, fear and neutral expression which were presented 40 times [9]. 128-channel EEG was used to continuously record data at 250 samples per second.…”

Section: B Experimental Paradigm With Typical and Asd Childrenmentioning

confidence: 99%

“…We show that when this synchronization index is used to build a connectivity map over the scalp corresponding to the maximum or minimum occurring synchrostates, its analysis using graphtheoretic measures effectively distinguishes between children with ASD and age matched controls when presented with faces with different emotions viz. fearful, happy and neutral, as studied in Apicella et al [9], leading to a possible new way of diagnosing ASD children from EEG analysis.…”

Section: Introductionmentioning

confidence: 99%

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Using brain connectivity measure of EEG synchrostates for discriminating typical and Autism Spectrum Disorder

Jamal

Das

Maharatna

et al. 2013

2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)

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Abstract-In this paper we utilized the concept of stable phase synchronization topography -synchrostates -over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from noninvasively recorded EEG data.

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The Social Neuroscience of Autism Spectrum Disorder

McPartland

Tillman

Yang

et al. 2014

Handbook of Autism and Pervasive Developmental Disorders, Fourth Edition

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This chapter reviews the neural systems subserving social information processing and their disruption in ASD. We focus on social perception, action understanding, and mental state reasoning, highlighting both conceptual background and recent advances. In addition, we review and discuss alternative accounts of the pathogenesis of autism, such as the role of atypical neural connectivity and its potentially detrimental effects on complex information processing. Building from current understanding of social neuroscience in ASD, we describe two key translational objectives for developing more effective strategies to promote well‐being in ASD in the future: developing assays for early detection of atypical brain function and formulating treatments to specifically target the brain systems hypothesized to underlie social and communicative impairments.

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Fusiform Gyrus responses to neutral and emotional faces in children with Autism Spectrum Disorders: a High Density ERP study

Cited by 57 publications

References 38 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Using brain connectivity measure of EEG synchrostates for discriminating typical and Autism Spectrum Disorder

The Social Neuroscience of Autism Spectrum Disorder

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