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1 – 10 of 322
Article
Publication date: 1 January 2014

Yujie Zhang, Zhuoxiang Ren and David Lautru

The resolution of electroencephalography (EEG) forward problem by the finite element method (FEM) involves the modeling of current dipoles with the singularities. The purpose of…

Abstract

Purpose

The resolution of electroencephalography (EEG) forward problem by the finite element method (FEM) involves the modeling of current dipoles with the singularities. The purpose of the paper is to investigate the accuracy issue of the two alternative methods, the direct method and the subtraction method for the modeling of current dipoles.

Design/methodology/approach

Finite element modeling of current dipoles using the direct method and the alternative implementations of the subtraction method.

Findings

The accuracy and the performance of different methods are compared through a four-layer spherical head model with available analytical solution. Results show that the subtraction method involving only the surface integrals provides the best accuracy.

Originality/value

The subtraction method removes the difficulty of modeling the singularity of current dipoles but the accuracy depends on the implementation.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 33 no. 1/2
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 16 November 2010

I.M.V. Caminiti, A. Formisano, R. Martone and F. Ferraioli

The purpose of this paper is to evaluate the performances of a resolution scheme able to follow the dynamics of brain tissue properties in combined ElectroEncefaloGraphic (EEG) …

Abstract

Purpose

The purpose of this paper is to evaluate the performances of a resolution scheme able to follow the dynamics of brain tissue properties in combined ElectroEncefaloGraphic (EEG) – MagnetoEncefaloGraphic (MEG) techniques for the brain analysis, minimizing the computation burden.

Design/methodology/approach

The estimation process in combined EEG‐MEG is performed by a Moore‐Penrose pseudo‐inverse computation. This is affected by the uncertain knowledge of the living tissues' electric properties. In principle, it is possible to estimate those properties from the EEG‐MEG signals. The estimation process becomes in this case non‐linear. A resolution scheme is proposed, based on the exploitation of the different dynamics characterizing sources and tissues properties.

Findings

The proposed resolution scheme provides a reasonable estimate of the sources for a computationally affordable frequency of non‐liner estimations.

Research limitations/implications

The proposed approach has not been tested yet on experimental data, and as such, its sensitivity to environmental uncertainty is not known yet.

Practical implications

The proposed strategy can be easily implemented to perform realistic measurement processing.

Originality/value

The paper presents a novel strategy to estimate tissues properties and EEG‐MEG signal sources based on the exploitation of their different dynamics, possibly taking advantages from an impedance tomography preliminary analysis for the tissue properties dynamics.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 29 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 1 November 2000

Jaroslav Mackerle

Gives a bibliographical review of the finite element methods (FEMs) applied in biomedicine from the theoretical as well as practical points of view. The bibliography at the end…

1347

Abstract

Gives a bibliographical review of the finite element methods (FEMs) applied in biomedicine from the theoretical as well as practical points of view. The bibliography at the end of the paper contains 748 references to papers, conference proceedings and theses/dissertations dealing with the finite element analyses and simulations in biomedicine that were published between 1985 and 1999.

Details

Engineering Computations, vol. 17 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 14 January 2022

Ashutosh Shankhdhar, Pawan Kumar Verma, Prateek Agrawal, Vishu Madaan and Charu Gupta

The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an…

Abstract

Purpose

The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.

Design/methodology/approach

This paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.

Findings

At the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.

Originality/value

BCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

Details

International Journal of Quality & Reliability Management, vol. 39 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 4 June 2020

Stamatis A. Amanatiadis, Georgios K. Apostolidis, Chrysanthi S. Bekiari and Nikolaos V. Kantartzis

The reliable transcranial imaging of brain inner structures for diagnostic purposes is deemed crucial owing to the decisive importance and contribution of the brain in human life…

Abstract

Purpose

The reliable transcranial imaging of brain inner structures for diagnostic purposes is deemed crucial owing to the decisive importance and contribution of the brain in human life. The purpose of this paper is to investigate the potential application of medical ultrasounds to transcranial imaging using advanced techniques, such as the total focussing method.

Design/methodology/approach

Initially, the fundamental details of the total focussing method are presented, while the skull properties, such as the increased acoustic velocity and scattering, are thoroughly examined. Although, these skull characteristics constitute the main drawback of typical transcranial ultrasonic propagation algorithms, they are exploited to focus the acoustic waves towards the brain. To this goal, a virtual source is designed, considering the wave refraction, to efficiently correct the reconstructed brain image. Finally, the verification of the novel method is conducted through numerical simulations of various realistic setups.

Findings

The theoretically designed virtual source resembles a focussed sensor; therefore, the directivity increment, owing to the propagation through the skull, is confirmed. Moreover, numerical simulations of real-world scenarios indicate that the typical artifacts of the conventional total focussing method are fully overcome because of the increased directivity of the proposed technique, while the reconstructed image is efficiently corrected when the proposed virtual source is used.

Originality/value

A new systematic methodology along with the design of a flexible virtual source is developed in this paper for the reliable and precise transcranial ultrasonic image reconstruction of the brain. Despite the slight degradation owing to the skull scattering, the combined application of the total focussing method and the featured virtual source can successfully detect arbitrary anomalies in the brain that cannot be spotted by conventional techniques.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 39 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 25 June 2020

Minghua Wei and Feng Lin

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper…

Abstract

Purpose

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

Design/methodology/approach

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

Findings

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

Originality/value

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Book part
Publication date: 5 December 2017

Sebastiano Massaro

In light of the growing interest in neuroscience within the managerial and organizational cognition (MOC) scholarly domain at large, this chapter advances current knowledge on…

Abstract

In light of the growing interest in neuroscience within the managerial and organizational cognition (MOC) scholarly domain at large, this chapter advances current knowledge on core neuroscience methods. It does so by building on the theoretical analysis put forward by Healey and Hodgkinson (2014, 2015), and by offering a thorough – yet accessible – methodological framework for a better understanding of key cognitive and social neuroscience methods. Classifying neuroscience methods based on their degree of resolution, functionality, and anatomical focus, the chapter outlines their features, practicalities, advantages and disadvantages. Specifically, it focuses on functional magnetic resonance imaging, electroencephalography, magnetoencephalography, heart rate variability, and skin conductance response. Equipped with knowledge of these methods, researchers will be able to further their understanding of the potential synergies between management and neuroscience, to better appreciate and evaluate the value of neuroscience methods, and to look at new ways to frame old and new research questions in MOC. The chapter also builds bridges between researchers and practitioners by rebalancing the hype and hopes surrounding the use of neuroscience in management theory and practice.

Details

Methodological Challenges and Advances in Managerial and Organizational Cognition
Type: Book
ISBN: 978-1-78743-677-0

Keywords

Book part
Publication date: 15 December 2015

Pierre A. Balthazard and Robert W. Thatcher

Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet…

Abstract

Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet interrelated paths to understand the relative contributions of anatomical and physiological mechanisms to the human brain–behavior relationship. From an organizational neuroscience perspective, the chapter describes over a dozen neuroimaging technologies that are classified under four groupings: morphologic, invasive metabolic, noninvasive metabolic, and electromagnetic. We then discuss neuroimaging variables that may be useful in social science investigations, and we underscore electroencephalography as a particularly useful modality for the study of individuals and groups in organizational settings. The chapter concludes by considering emerging science and novel brain technologies for the organizational researcher as we look to the future.

Details

Organizational Neuroscience
Type: Book
ISBN: 978-1-78560-430-0

Keywords

Article
Publication date: 17 October 2008

Li‐Wei Wu, Hsien‐Cheng Liao, Jwu‐Sheng Hu and Pei‐Chen Lo

This paper aims to present a novel embedded‐internet robot system based on an internet robot agent and the brain‐computer interface (BCI) scheme.

Abstract

Purpose

This paper aims to present a novel embedded‐internet robot system based on an internet robot agent and the brain‐computer interface (BCI) scheme.

Design/methodology/approach

A highly flexible and well‐integrated embedded ethernet robot (eRobot) was designed with enhanced mobility. In the eRobot, a circuit core module called a tiny network bridge (TNB) is designed to reduce robotic system cost and increase its mobility and developmental flexibility. The TNB enables users to control eRobot motion via embedded ethernet technology. Through electroencephalogram (EEG) feedback training, the command translation unit (CTU) and alertness level detection unit (ADU) allow the eRobot to perform specific motions (for example, lying down or standing up) to reflect alertness levels of the user, and move forward, turn left or right following the user's command.

Findings

After a short training period, subjects could achieve at least 70 percent accuracy in the CTU game testing. And the error rate of ADU, estimated from the results of classifying 496 labeled EEG epochs, was approximately 10.7 percent. Combining an encoding procedure, the commands issued from the CTU could prevent the robot from performing undesired actions.

Originality/value

The eRobot could reflect some physiological human states and be controlled by users with our economical design and only two bipolar EEG channels adopted. Thus, users could make the EEG‐based eRobot agent his or her representative. Based on the proposed EEG‐based eRobot system, a robot with increased sophistication will be developed in the future for use by disabled patients.

Details

Industrial Robot: An International Journal, vol. 35 no. 6
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 26 January 2022

Rajashekhar U., Neelappa and Harish H.M.

The natural control, feedback, stimuli and protection of these subsequent principles founded this project. Via properly conducted experiments, a multilayer computer rehabilitation…

Abstract

Purpose

The natural control, feedback, stimuli and protection of these subsequent principles founded this project. Via properly conducted experiments, a multilayer computer rehabilitation system was created that integrated natural interaction assisted by electroencephalogram (EEG), which enabled the movements in the virtual environment and real wheelchair. For blind wheelchair operator patients, this paper involved of expounding the proper methodology. For educating the value of life and independence of blind wheelchair users, outcomes have proven that virtual reality (VR) with EEG signals has that potential.

Design/methodology/approach

Individuals face numerous challenges with many disorders, particularly when multiple dysfunctions are diagnosed and especially for visually effected wheelchair users. This scenario, in reality, creates in a degree of incapacity on the part of the wheelchair user in terms of performing simple activities. Based on their specific medical needs, confined patients are treated in a modified method. Independent navigation is secured for individuals with vision and motor disabilities. There is a necessity for communication which justifies the use of VR in this navigation situation. For the effective integration of locomotion besides, it must be under natural guidance. EEG, which uses random brain impulses, has made significant progress in the field of health. The custom of an automated audio announcement system modified to have the help of VR and EEG for the training of locomotion and individualized interaction of wheelchair users with visual disability is demonstrated in this study through an experiment. Enabling the patients who were otherwise deemed incapacitated to participate in social activities, as the aim was to have efficient connections.

Findings

To protect their life straightaway and to report all these disputes, the military system should have high speed, more precise portable prototype device for nursing the soldier health, recognition of solider location and report about health sharing system to the concerned system. Field programmable gate array (FPGA)-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals, the soldier’s health is observed on systematic bases. By emerging Verilog hardware description language (HDL) programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t the whole work is approved in a Vivado Design Suite. Classification of different abnormalities and cloud storage of EEG along with the type of abnormalities, artifact elimination, abnormalities identification based on feature extraction, exist in the segment of suggested architecture. Irregularity circumstances are noticed through developed prototype system and alert the physically challenged (PHC) individual via an audio announcement. An actual method for eradicating motion artifacts from EEG signals that have anomalies in the PHC person’s brain has been established, and the established system is a portable device that can deliver differences in brain signal variation intensity. Primarily the EEG signals can be taken and the undesirable artifact can be detached, later structures can be mined by discrete wavelet transform these are the two stages through which artifact deletion can be completed. The anomalies in signal can be noticed and recognized by using machine learning algorithms known as multirate support vector machine classifiers when the features have been extracted using a combination of hidden Markov model (HMM) and Gaussian mixture model (GMM). Intended for capable declaration about action taken by a blind person, these result signals are protected in storage devices and conveyed to the controller. Pretending daily motion schedules allows the pretentious EEG signals to be caught. Aimed at the validation of planned system, the database can be used and continued with numerous recorded signals of EEG. The projected strategy executes better in terms of re-storing theta, delta, alpha and beta complexes of the original EEG with less alteration and a higher signal to noise ratio (SNR) value of the EEG signal, which illustrates in the quantitative analysis. The projected method used Verilog HDL and MATLAB software for both formation and authorization of results to yield improved results. Since from the achieved results, it is initiated that 32% enhancement in SNR, 14% in mean squared error (MSE) and 65% enhancement in recognition of anomalies, hence design is effectively certified and proved for standard EEG signals data sets on FPGA.

Originality/value

The proposed system can be used in military applications as it is high speed and excellent precise in terms of identification of abnormality, the developed system is portable and very precise. FPGA-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals the soldier health is observed in systematic bases. The proposed system is developed using Verilog HDL programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t and synthesised using in Vivado Design Suite software tool.

Details

International Journal of Pervasive Computing and Communications, vol. 19 no. 3
Type: Research Article
ISSN: 1742-7371

Keywords

1 – 10 of 322