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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…

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

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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 …

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

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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…

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

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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…

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

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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…

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

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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…

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

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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

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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

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Article
Publication date: 30 September 2020

Li Xiaoling

In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel…

Abstract

Purpose

In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel classification algorithm for motor imagery based on temporal and spatial characteristics extracted by using convolutional neural networks (TS-CNN) model.

Design/methodology/approach

According to the proposed algorithm, a five-layer neural network model was constructed to classify the electroencephalogram (EEG) signals. Firstly, the author designed a motor imagery-based BCI experiment, and four subjects were recruited to participate in the experiment for the recording of EEG signals. Then, after the EEG signals were preprocessed, the temporal and spatial characteristics of EEG signals were extracted by longitudinal convolutional kernel and transverse convolutional kernels, respectively. Finally, the classification of motor imagery was completed by using two fully connected layers.

Findings

To validate the classification performance and efficiency of the proposed algorithm, the comparative experiments with the state-of-the-arts algorithms are applied to validate the proposed algorithm. Experimental results have shown that the proposed TS-CNN model has the best performance and efficiency in the classification of motor imagery, reflecting on the introduced accuracy, precision, recall, ROC curve and F-score indexes.

Originality/value

The proposed TS-CNN model accurately recognized the EEG signals for different tasks of motor imagery, and provided theoretical basis and technical support for the application of BCI control system in the field of rehabilitation exoskeleton.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 4
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 1 September 2005

Xinshan Ma and Xin Guan

The electroencephalography (EEG) source tomography in bio‐electromagnetics is to estimate current dipole sources inside the brain from the measured electric potential…

Abstract

Purpose

The electroencephalography (EEG) source tomography in bio‐electromagnetics is to estimate current dipole sources inside the brain from the measured electric potential distribution on the scalp surface. A traditional algorithm is the low‐resolution electromagnetic tomography algorithm (LORETA). In order to obtain high‐resolution tomography, the LORETA‐contracting algorithm is proposed.

Design/methodology/approach

The relation between the dipolar current source J at the nodes in source region and the potential U at the observed points on the scalp surface can be expressed as a matrix equation U=KJ after discretization. K is a coefficient matrix. Usually its simultaneous equation is an under‐determined system. The LORETA approach is to find out min‖BWJ2, under constraint U=KJ where B is the discrete Laplacian operator matrix, W is a weighting diagonal matrix. Its solution is J=(WBTBW)−1KT{K(WBTBW)−1KT}+U where {}+ denotes the Moore‐Penrose pseudo‐inverse matrix. The improvement on this approach is to establish an iterative program to repeat LORETA and reduce the number of unknown J quantities in the step i+1 by contracting the source region excluding some extreme little quantities of J given in the step i. The simultaneous equations will gradually turn to a properly determined system or to an over‐determined system. Finally, its solution can be obtained by using the least square method.

Findings

Repeating to make the low‐resolution tomography by contracting the source region, we can get a high‐resolution tomography easily.

Research limitations/implications

The LORETA‐contracting algorithm is based on the assumption that the dipolar current sources inside the brain are sparse and concentrated based on the physiological study of the brain activity.

Originality/value

It is new to repeat LORETA combined with the contracting technique. This algorithm can be developed to solve EEG problems of realistic head models.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 24 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

1 – 10 of 227