A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

Ju, Ziyi; Li, Gun; Hussain, Amir; Mahmud, Mufti; Ieracitano, Cosimo

doi:10.3390/app10196761

Cited by 8 publications

(6 citation statements)

References 68 publications

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“…To conclude, the combination of eye tracking, visualization, and ML may hold considerable potential for the development of an objective tool to assist the diagnosis of ASD. These results can be used and new data analyzed to create a screening tool for health professionals Further, features related to the dynamics of eye movement can also be considered as candidate features for developing predictive models, and recently published deep neural network methodologies can be adapted to our model [45]. Eye-tracking measures which require limited technical expertise can be quickly managed during diagnostic interviews.…”

Section: Discussionmentioning

confidence: 99%

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

Cilia¹,

Carette²,

Elbattah³

et al. 2021

JMIR Hum Factors

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Background The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. Objective This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening Methods The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. Results The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. Conclusions Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders.

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Section: Discussionmentioning

confidence: 99%

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

Cilia¹,

Carette²,

Elbattah³

et al. 2021

JMIR Hum Factors

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“…If BCI systems based on the P 300 are developed mainly for people with severe motor and sensory impairments, a prototype BCI for wheelchair control using decoded EEG signals from the user can serve as a certain point of contact. The prototype includes a shadow detection module based on an adaptive directional tracking filter to extract target features along the direction of the boundaries and offers a theoretical basis for wheelchair movement control [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the P 300 component of visual evoked potentials, a BCI system is proposed for automatic control of the movement of wheelchairs and the detection of shadows along the route using decoded EEG signals from the user while driving the wheelchair [ 19 ]. The proposed algorithm significantly improves the efficiency and accuracy of shadow objects detection.…”

Section: Introductionmentioning

confidence: 99%

Physiological and Medico-Social Research Trends of the Wave P300 and More Late Components of Visual Event-Related Potentials

Lytaev

Vatamaniuk

2021

Brain Sciences

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To extend the application of the late waves of the event-related potentials (ERPs) to multiple modalities, devices and software the underlying physiological mechanisms and responses of the brain for a particular sensory system and mental function must be carefully examined. The objective of this study was aimed to study the sensory processes of the “human-computer interaction” model when classifying visual images with an incomplete set of signs based on the analysis of early, middle, late and slow ERPs components. 26 healthy subjects (men) aged 20–26 years were investigated. ERPs in 19 monopolar sites according to the 10/20 system were recorded. Discriminant and factor analyzes (BMDP Statistical Software) were applied. The component N450 is the most specialized indicator of the perception of unrecognizable (oddball) visual images. The amplitude of the ultra-late components N750 and N900 is also higher under conditions of presentation of the oddball image, regardless of the location of the registration points. In brain pathology along with the pronounced asymmetry of the wave distribution, reduction of the N150 wave and lengthening of its peak latency, a line of regularities were noted. These include–a pronounced reduction in peak latency P250 and N350, an increased amplitude of N350 in the frontal and central points of registration, a decrease in the amplitude of N450 in the left frontal cortex and its increase in the occipital registration points, activation of the occipital cortex at a time interval of 400–500 ms, as well as fusion later waves. We called such phenomena of the development of cognitive ERP in brain pathology “the incongruence of ERP components”. The results of the research are discussed in the light of the paradigm of the P300 wave application in brain-computer interface systems, as well as with the peculiarities in brain pathology.

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“…However, some potential alternatives remain to be found. The latest research in image understanding [40] and image segmentation [41,42] will be helpful to further improve the performance of machine aesthetic models. Moreover, the technology of deep learning [43][44][45][46] should also be considered to build machine aesthetic models.…”

Section: Limitation Of the Proposed Approachmentioning

confidence: 99%

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

Peng

Wang

et al. 2021

Information

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Imitation of human behaviors is one of the effective ways to develop artificial intelligence. Human dancers, standing in front of a mirror, always achieve autonomous aesthetics evaluation on their own dance motions, which are observed from the mirror. Meanwhile, in the visual aesthetics cognition of human brains, space and shape are two important visual elements perceived from motions. Inspired by the above facts, this paper proposes a novel mechanism of automatic aesthetics evaluation of robotic dance motions based on multiple visual feature integration. In the mechanism, a video of robotic dance motion is firstly converted into several kinds of motion history images, and then a spatial feature (ripple space coding) and shape features (Zernike moment and curvature-based Fourier descriptors) are extracted from the optimized motion history images. Based on feature integration, a homogeneous ensemble classifier, which uses three different random forests, is deployed to build a machine aesthetics model, aiming to make the machine possess human aesthetic ability. The feasibility of the proposed mechanism has been verified by simulation experiments, and the experimental results show that our ensemble classifier can achieve a high correct ratio of aesthetics evaluation of 75%. The performance of our mechanism is superior to those of the existing approaches.

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A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

Cited by 8 publications

References 68 publications

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

Physiological and Medico-Social Research Trends of the Wave P300 and More Late Components of Visual Event-Related Potentials

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

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