Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental disorder involving core deficits in interpersonal communication and social interactions, usually associated with restricted or repetitive mannerisms and interests. Interventions such as physical training and special education to ASD children are mandatory to remediate these deficits. As significant developmental differences exist among individuals with ASD, monitoring the cognitive process is the key to understanding the effectiveness of the intervention. Traditionally, cognition assessment relies on behavior observation and scales, which often result in severe bias because the improvement (or evolution in a more general sense) of the subject's capability may not be extrinsically reflected by the behavior.
In order to portray the intrinsic cognitive process of the ASD individuals, contemporary technologies rooted from signal processing community and Artificial Intelligence have emerged to analyze (1) visual signals, i.e., head pose, eye gaze, and facial expressions, to characterize one's attention and emotions, and (2) psychological responses, i.e., electrodermal activities and electroencephalogram (EEG). Those technologies focus on characterizing the cognitive states and /or affective states as the latter directly influence the former and can be explicitly interpreted via computer vision technologies. In order to access one’s cognitive process more comprehensively, it is desirable to jointly access the cognitive states and affective states in both qualitative and quantitative manners. In other words, multi-modal methods will prevail to extract the salient information and fuse them to better understand the cognitive process during/after the intervention.
This Research Topic welcomes original research and review articles to provide tentative answers to the 2 major questions: (1) how the intrinsic cognition mechanism evolves in accordance with the intervention? and (2) what changes to the intrinsic cognition mechanism may improve the extrinsic behavior? In more detail, this Research Topic aims to explore the cognition process of ASD children with a multi-modal signal process. Algorithms, methods, and empirical study for monitoring the cognitive process of ASD children are all welcome. Sub-themes include but are not limited to the following:
(1) Visual and voice signals based behavioral monitoring,
(2) Gaze and eye-tracking methodology for cognitive activity evaluation,
(3) EEG based cognition process analyses,
(4) Event-related potential technique for ASD children,
(5) Multi-modal fusion methods for cognition process analyses,
(6) Empirical study on intrinsic cognition mechanism and extrinsic behavior.
Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental disorder involving core deficits in interpersonal communication and social interactions, usually associated with restricted or repetitive mannerisms and interests. Interventions such as physical training and special education to ASD children are mandatory to remediate these deficits. As significant developmental differences exist among individuals with ASD, monitoring the cognitive process is the key to understanding the effectiveness of the intervention. Traditionally, cognition assessment relies on behavior observation and scales, which often result in severe bias because the improvement (or evolution in a more general sense) of the subject's capability may not be extrinsically reflected by the behavior.
In order to portray the intrinsic cognitive process of the ASD individuals, contemporary technologies rooted from signal processing community and Artificial Intelligence have emerged to analyze (1) visual signals, i.e., head pose, eye gaze, and facial expressions, to characterize one's attention and emotions, and (2) psychological responses, i.e., electrodermal activities and electroencephalogram (EEG). Those technologies focus on characterizing the cognitive states and /or affective states as the latter directly influence the former and can be explicitly interpreted via computer vision technologies. In order to access one’s cognitive process more comprehensively, it is desirable to jointly access the cognitive states and affective states in both qualitative and quantitative manners. In other words, multi-modal methods will prevail to extract the salient information and fuse them to better understand the cognitive process during/after the intervention.
This Research Topic welcomes original research and review articles to provide tentative answers to the 2 major questions: (1) how the intrinsic cognition mechanism evolves in accordance with the intervention? and (2) what changes to the intrinsic cognition mechanism may improve the extrinsic behavior? In more detail, this Research Topic aims to explore the cognition process of ASD children with a multi-modal signal process. Algorithms, methods, and empirical study for monitoring the cognitive process of ASD children are all welcome. Sub-themes include but are not limited to the following:
(1) Visual and voice signals based behavioral monitoring,
(2) Gaze and eye-tracking methodology for cognitive activity evaluation,
(3) EEG based cognition process analyses,
(4) Event-related potential technique for ASD children,
(5) Multi-modal fusion methods for cognition process analyses,
(6) Empirical study on intrinsic cognition mechanism and extrinsic behavior.
