scholarly journals Automatic Detection of Fiducial Landmarks Toward the Development of an Application for Digitizing the Locations of EEG Electrodes: Occipital Structure Sensor-Based Work

2021 ◽  
Vol 15 ◽  
Author(s):  
Elieser E. Gallego Martínez ◽  
Anisleidy González Mitjans ◽  
Eduardo Garea-Llano ◽  
Maria L. Bringas-Vega ◽  
Pedro A. Valdes-Sosa
Keyword(s):  
Automatic Detection ◽  
Human Head ◽  
Facial Feature ◽  
Initial Step ◽  
Experimental Results ◽  
Head Model ◽  
Anatomical Landmarks ◽  
Geometric Transformations ◽  
Spatial Coordinates ◽  
Structure Sensor

The reconstruction of electrophysiological sources within the brain is sensitive to the constructed head model, which depends on the positioning accuracy of anatomical landmarks known as fiducials. In this work, we propose an algorithm for the automatic detection of fiducial landmarks of EEG electrodes on the 3D human head model. Our proposal combines a dimensional reduction approach with a perspective projection from 3D to 2D object space; the eye and ear automatic detection in a 2D face image by two cascades of classifiers and geometric transformations to obtain 3D spatial coordinates of the landmarks and to generate the head coordinate system, This is accomplished by considering the characteristics of the scanner information. Capturing the 3D model of the head is done with Occipital Inc. ST01 structure sensor and the implementation of our algorithm was carried out on MATLAB R2018b using the Computer Vision Toolbox and the FieldTrip Toolbox. The experimental results were aimed at recursively exploring the efficacy of the facial feature detectors as a function of the projection angle; they show that robust results are obtained in terms of false acceptance rate. Our proposal is an initial step of an approach for the automatic digitization of electrode locations. The experimental results demonstrate that the proposed method detects anatomical facial landmarks automatically, accurately, and rapidly.

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

scholarly journals Neural silences can be localized rapidly using noninvasive scalp EEG

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alireza Chamanzar ◽  
Marlene Behrmann ◽  
Pulkit Grover
Keyword(s):  
Center Of Mass ◽  
Cost Effective ◽  
Human Head ◽  
Head Model ◽  
Cortical Function ◽  
Scalp Eeg ◽  
Localization Algorithms ◽  
Important Benefit ◽  
Eeg Recordings ◽  
Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

scholarly journals Levels of detail analysis of microwave scattering from human head models for brain stroke detection

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4061 ◽  
Author(s):  
Awais Munawar Qureshi ◽  
Zartasha Mustansar
Keyword(s):  
Scattering Problem ◽  
Human Head ◽  
Computational Time ◽  
Head Model ◽  
Microwave Scattering ◽  
Levels Of Detail ◽  
Realistic Head Model ◽  
Different Types ◽  
Brain Stroke ◽  
Brain Tissues

In this paper, we have presented a microwave scattering analysis from multiple human head models. This study incorporates different levels of detail in the human head models and its effect on microwave scattering phenomenon. Two levels of detail are taken into account; (i) Simplified ellipse shaped head model (ii) Anatomically realistic head model, implemented using 2-D geometry. In addition, heterogenic and frequency-dispersive behavior of the brain tissues has also been incorporated in our head models. It is identified during this study that the microwave scattering phenomenon changes significantly once the complexity of head model is increased by incorporating more details using magnetic resonance imaging database. It is also found out that the microwave scattering results match in both types of head model (i.e., geometrically simple and anatomically realistic), once the measurements are made in the structurally simplified regions. However, the results diverge considerably in the complex areas of brain due to the arbitrary shape interface of tissue layers in the anatomically realistic head model.After incorporating various levels of detail, the solution of subject microwave scattering problem and the measurement of transmitted and backscattered signals were obtained using finite element method. Mesh convergence analysis was also performed to achieve error free results with a minimum number of mesh elements and a lesser degree of freedom in the fast computational time. The results were promising and the E-Field values converged for both simple and complex geometrical models. However, the E-Field difference between both types of head model at the same reference point differentiated a lot in terms of magnitude. At complex location, a high difference value of 0.04236 V/m was measured compared to the simple location, where it turned out to be 0.00197 V/m. This study also contributes to provide a comparison analysis between the direct and iterative solvers so as to find out the solution of subject microwave scattering problem in a minimum computational time along with memory resources requirement.It is seen from this study that the microwave imaging may effectively be utilized for the detection, localization and differentiation of different types of brain stroke. The simulation results verified that the microwave imaging can be efficiently exploited to study the significant contrast between electric field values of the normal and abnormal brain tissues for the investigation of brain anomalies. In the end, a specific absorption rate analysis was carried out to compare the ionizing effects of microwave signals to different types of head model using a factor of safety for brain tissues. It is also suggested after careful study of various inversion methods in practice for microwave head imaging, that the contrast source inversion method may be more suitable and computationally efficient for such problems.

Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks

2020 ◽  
Vol 396 ◽  
pp. 514-521 ◽  
Author(s):  
Xulei Yang ◽  
Wai Teng Tang ◽  
Gabriel Tjio ◽  
Si Yong Yeo ◽  
Yi Su
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Detection ◽  
Anatomical Landmarks ◽  
Mr Scanning

Efficient Electromagnetic Analysis of a Dispersive Head Model Due to Smart Glasses Embedded Antennas at Wi-Fi and 5G Frequencies

2021 ◽  
Vol 36 (2) ◽  
pp. 159-167
Author(s):  
Fatih Kaburcuk ◽  
Atef Elsherbeni
Keyword(s):  
Numerical Study ◽  
Computation Time ◽  
Human Head ◽  
Head Model ◽  
Computer Memory ◽  
Large Computer ◽  
Smart Glasses ◽  
Embedded Antennas ◽  
Human Head Model ◽  
Difference Time

Numerical study of electromagnetic interaction between an adjacent antenna and a human head model requires long computation time and large computer memory. In this paper, two speeding up techniques for a dispersive algorithm based on finite-difference time-domain method are used to reduce the required computation time and computer memory. In order to evaluate the validity of these two speeding up techniques, specific absorption rate (SAR) and temperature rise distributions in a dispersive human head model due to radiation from an antenna integrated into a pair of smart glasses are investigated. The antenna integrated into the pair of smart glasses have wireless connectivity at 2.4 GHz and 5th generation (5G) cellular connectivity at 4.9 GHz. Two different positions for the antenna integrated into the frame are considered in this investigation. These techniques provide remarkable reduction in computation time and computer memory.

Computational Modeling of Combat Helmet Performance Analysis Integrating Blunt and Blast Loadings

2020 ◽  
Author(s):  
X. Gary Tan ◽  
Amit Bagchi
Keyword(s):  
System Analysis ◽  
Plastic Material ◽  
Human Head ◽  
Material Model ◽  
Head Model ◽  
Ballistic Impacts ◽  
Rate Dependent ◽  
Brain Responses ◽  
Suspension Geometry ◽  
System Configurations

Abstract Combat helmets have gone through many changes, from shells made of metal to advanced composites using Kevlar and Dyneema, along with introduction of pad suspensions to provide comfort and protection. Helmets have been designed to perform against ballistic and blunt impact threats. But, in today’s warfare, combat helmets are expected to protect against all three threats, blunt, ballistic impacts and blast effects to minimize traumatic brain injury (TBI) and provide a better thermal comfort. We are developing a helmet system analysis methodology integrating the effect of multiple threats, i.e., blast and blunt impacts, to achieve an optimal helmet system design, by utilizing multi-physics computational tools. We used a validated human head model to represent the warfighter’s head. The helmet composite shell was represented by an orthotropic elasto-plastic material model. A strain rate dependent model was employed for pad suspension material. Available dynamic loading data was used to calibrate the material parameters. Multiple helmet system configurations subjected to blast and blunt loadings were considered to quantify their influence on brain biomechanical response. Parametric studies were carried out to assess energy absorption for different suspension geometry and material morphology for different loadings. The resulting brain responses were used with published injury criteria to characterize the helmet system performance through a single metric for each threat type. Approaches to combine single-threat metrics to allow aggregating performance against multiple threats were discussed.

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