Tele Tan

Face Recognition and Visual Search Strategies in Autism Spectrum Disorders: Amending and Extending a Recent Review by Weigelt et al.

The purpose of this review was to build upon a recent review by Weigelt et al. which examined visual search strategies and face identification between individuals with autism spectrum disorders (ASD) and typically developing peers. Seven databases, CINAHL Plus, EMBASE, ERIC, Medline, Proquest, PsychInfo and PubMed were used to locate published scientific studies matching our inclusion criteria. A total of 28 articles not included in Weigelt et al. met criteria for inclusion into this systematic review. Of these 28 studies, 16 were available and met criteria at the time of the previous review, but were mistakenly excluded; and twelve were recently published. Weigelt et al. found quantitative, but not qualitative, differences in face identification in individuals with ASD. In contrast, the current systematic review found both qualitative and quantitative differences in face identification between individuals with and without ASD. There is a large inconsistency in findings across the eye tracking and neurobiological studies reviewed. Recommendations for future research in face recognition in ASD were discussed.

An improved P300 extraction using ICA-R for P300-BCI speller

In this study, a new P300 extraction method is investigated by using a form of constrained independent component analysis (cICA) algorithm called one-unit ICA-with-reference (ICA-R) which extracts the P300 signal based on its temporal information. The main advantage of this method compared to the existing ICA-based method is that the desired P300 signal is extracted directly without requiring partial or full signal decomposition and any post-processing on the outcome of the ICA before the P300 signal can be obtained. Since only one IC is extracted, the method is computationally more efficient for real-time P300 BCI applications. In our study, when tested on the BCI competition 2003 dataset IIb, the current state-of-the-art performance is maintained by using the one-unit ICA-R. Besides that, the ability of the method to visualize P300 signals at the single-trial level also suggests it has potential applications in other types of ERP studies.

Mechanisms of facial emotion recognition in autism spectrum disorders : Insights from eye tracking and electroencephalography

HIGHLIGHTSIndividuals with Autism Spectrum Disorder present with atypical gaze and cortical activation to facially expressed emotions.Individuals with Autism Spectrum Disorder may use compensatory strategies during facial emotion recognition.Individuals with Autism Spectrum Disorder may employ self‐regulatory strategies during facial emotion recognition.Eye tracking and electroencephalography findings may provide potential markers for diagnosis and treatment targets. ABSTRACT While behavioural difficulties in facial emotion recognition (FER) have been observed in individuals with Autism Spectrum Disorder (ASD), behavioural studies alone are not suited to elucidate the specific nature of FER challenges in ASD. Eye tracking (ET) and electroencephalography (EEG) provide insights in to the attentional and neurological correlates of performance, and may therefore provide insight in to the mechanisms underpinning FER in ASD. Given that these processes develop over the course of the developmental trajectory, there is a need to synthesise findings in regard to the developmental stages to determine how the maturation of these systems may impact FER in ASD. We conducted a systematic review of fifty‐four studies investigating ET or EEG meeting inclusion criteria. Findings indicate divergence of visual processing pathways in individuals with ASD. Altered function of the social brain in ASD impacts the processing of facial emotion across the developmental trajectory, resulting in observable differences in ET and EEG outcomes.

Video shot boundary detection based on candidate segment selection and transition pattern analysis

Video shot boundary detection or shot segmentation is an integral part of semantic video analysis. The objective of this process is to automatically detect the boundary region in video that further segment the video into meaningful shot, scene and so on. Video frame feature representation therefore plays an important role in the process where it directly affects the overall performance of the system. The transition points between meaningful scenes can be emphasised by the extracted features. In this paper, a combination of global and local feature descriptors is implemented to represent the temporal characteristic in video. Motivated by computational efficiency and practical implementation, a video shot boundary detection scheme using adaptive thresholding is proposed. Candidate segment selection and transition pattern analysis are implemented by the dissimilarity score between video frames. The performance evaluation is constructed on a golf video dataset using the precision and recall performance measures.

Multi-Modal Visual Features-Based Video Shot Boundary Detection

One of the essential pre-processing steps of semantic video analysis is the video shot boundary detection (SBD). It is the primary step to segment the sequence of video frames into shots. Many SBD systems using supervised learning have been proposed for years; however, the training process still remains its principal limitation. In this paper, a multi-modal visual features-based SBD framework is employed that aims to analyze the behaviors of visual representation in terms of the discontinuity signal. We adopt a candidate segment selection that performs without the threshold calculation but uses the cumulative moving average of the discontinuity signal to identify the position of shot boundaries and neglect the non-boundary video frames. The transition detection is structurally performed to distinguish candidate segment into a cut transition and a gradual transition, including fade in/out and logo occurrence. Experimental results are evaluated using the golf video clips and the TREC2001 documentary video data set. Results show that the proposed SBD framework can achieve good accuracy in both types of video data set compared with other proposed SBD methods.

Single-trial event-related potential extraction through one-unit ICA-with-reference

OBJECTIVE In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. APPROACH In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. MAIN RESULTS Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. SIGNIFICANCE In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

Belongingness in Early Secondary School: Key Factors that Primary and Secondary Schools Need to Consider

It is unknown if, and how, students redefine their sense of school belongingness after negotiating the transition to secondary school. The current study used longitudinal data from 266 students with, and without, disabilities who negotiated the transition from 52 primary schools to 152 secondary schools. The study presents the 13 most significant personal student and contextual factors associated with belongingness in the first year of secondary school. Student perception of school belongingness was found to be stable across the transition. No variability in school belongingness due to gender, disability or household-socio-economic status (SES) was noted. Primary school belongingness accounted for 22% of the variability in secondary school belongingness. Several personal student factors (competence, coping skills) and school factors (low-level classroom task-goal orientation), which influenced belongingness in primary school, continued to influence belongingness in secondary school. In secondary school, effort-goal orientation of the student and perception of their school’s tolerance to disability were each associated with perception of school belongingness. Family factors did not influence belongingness in secondary school. Findings of the current study highlight the need for primary schools to foster belongingness among their students at an early age, and transfer students’ belongingness profiles as part of the hand-over documentation. Most of the factors that influenced school belongingness before and after the transition to secondary are amenable to change.

The Personal and Contextual Contributors to School Belongingness among Primary School Students

School belongingness has gained currency among educators and school health professionals as an important determinant of adolescent health. The current cross-sectional study presents the 15 most significant personal and contextual factors that collectively explain 66.4% (two-thirds) of the variability in 12-year old students’ perceptions of belongingness in primary school. The study is part of a larger longitudinal study investigating the factors associated with student adjustment in the transition from primary to secondary school. The study found that girls and students with disabilities had higher school belongingness scores than boys, and their typically developing counterparts respectively; and explained 2.5% of the variability in school belongingness. The majority (47.1% out of 66.4%) of the variability in school belongingness was explained by student personal factors, such as social acceptance, physical appearance competence, coping skills, and social affiliation motivation; followed by parental expectations (3% out of 66.4%), and school-based factors (13.9% out of 66.4%) such as, classroom involvement, task-goal structure, autonomy provision, cultural pluralism, and absence of bullying. Each of the identified contributors of primary school belongingness can be shaped through interventions, system changes, or policy reforms.

Closed-loop Petri Net model for implementing an affective-state expressive robotic face

A closed-loop Petri Net (PN) model was developed to exhibit, maintain and, withdraw facial expressions of six basic affective states, in a human-like manner, on a robotic face. The PN model was aimed to enable execution of major facial muscles-like-interactions between segments of a latex-made facial mask. The muscle-like interactions were based upon the widely accepted and used facial action coding systems. In order to validate the PN model, the facial mask, mounted on a 3-D printed artificial skull, was used as a robotic face. Human facial muscles like movements, generated on the surface of the facial mask, were able to express positive and negative affective states. Audio signals were used as stimuli for eliciting expressions of affective states. Results show that an event driven discrete model would suffice deterministic representation of a finite number of affective states on an artificial robotic face.

An EEG coherence-based analysis approach for investigating response conflict processes in 7 and 9-year old children

Understanding the development of the brains neural networks can reveal critical insights into the cognitive changes that occur from infancy to late childhood. Behavioural metrics including: task accuracy, stimuli recognition, and reaction time show dramatic changes over childhood. In this study we investigated response control using the Erikson Flanker task. In a dataset of 45 EEG recordings, we calculated spectral coherence to measure connectivity between all possible electrode pairs. Coherence measures were performed on two different trial conditions -congruent (where there is no response conflict) and incongruent (where response conflict is induced). The increase in incongruent coherence compared to the congruent was investigated for each electrode pair over 45 healthy subjects aged seven years. The same calculation was then performed on the same group of subjects two years later when they were aged nine years. The results revealed that at age seven years, increased coherence was detected in the left prefrontal to right and left parieto-occipital - i.e. an anatomical region located between the parietal and occipital lobes - within theta band. No increase was found for the older group-at age nine years- which may indicate cognitive development in conflict processing mechanism.