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Article
Publication date: 23 December 2022

Jinchao Huang

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise…

Abstract

Purpose

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise electroencephalogram (EEG) signals are collected under the interference of noises. However, the conventional ConvNet model cannot directly solve this problem. This study aims to discuss the aforementioned issues.

Design/methodology/approach

To solve this problem, this paper adopted a novel residual shrinkage block (RSB) to construct the ConvNet model (RSBConvNet). During the feature extraction from EEG signals, the proposed RSBConvNet prevented the noise component in EEG signals, and improved the classification accuracy of motor imagery. In the construction of RSBConvNet, the author applied the soft thresholding strategy to prevent the non-related motor imagery features in EEG signals. The soft thresholding was inserted into the residual block (RB), and the suitable threshold for the current EEG signals distribution can be learned by minimizing the loss function. Therefore, during the feature extraction of motor imagery, the proposed RSBConvNet de-noised the EEG signals and improved the discriminative of classification features.

Findings

Comparative experiments and ablation studies were done on two public benchmark datasets. Compared with conventional ConvNet models, the proposed RSBConvNet model has obvious improvements in motor imagery classification accuracy and Kappa coefficient. Ablation studies have also shown the de-noised abilities of the RSBConvNet model. Moreover, different parameters and computational methods of the RSBConvNet model have been tested on the classification of motor imagery.

Originality/value

Based on the experimental results, the RSBConvNet constructed in this paper has an excellent recognition accuracy of MI-BCI, which can be used for further applications for the online MI-BCI.

Details

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

Keywords

Book part
Publication date: 22 November 2021

Ruchi Sinha, Louise Kyriaki, Zachariah R. Cross, Imogen E. Weigall and Alex Chatburn

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making…

Abstract

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

Article
Publication date: 7 April 2022

Tian-Jian Luo

Steady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to…

Abstract

Purpose

Steady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.

Design/methodology/approach

To survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.

Findings

For the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.

Originality/value

Based on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

Details

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

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…

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. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 20 October 2021

Jayalaxmi Anem, G. Sateeshkumar and R. Madhu

The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially…

44

Abstract

Purpose

The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially, pre-processing is done on EEG signal for quality improvement. Then, by using wavelet transform (WT) feature extraction is done. The artefacts present in the EEG are removed using deep convLSTM. This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.

Design/methodology/approach

Nowadays' EEG signals play vital role in the field of neurophysiologic research. Brain activities of human can be analysed by using EEG signals. These signals are frequently affected by noise during acquisition and other external disturbances, which lead to degrade the signal quality. Denoising of EEG signals is necessary for the effective usage of signals in any application. This paper proposes a new technique named as flower pollination fractional calculus optimisation (FPFCO) algorithm for the removal of artefacts from EEG signal through deep learning scheme. FPFCO algorithm is the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM. The existed FPO algorithm is used for solution update through global and local pollinations. In this case, the fractional calculus (FC) method attempts to include the past solution by including the second order derivative. As a result, the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization (FPO) method. Initially, 5 EEG signals are contaminated by artefacts such as EMG, EOG, EEG and random noise. These contaminated EEG signals are pre-processed to remove baseline and power line noises. Further, feature extraction is done by using WT and extracted features are applied to deep convLSTM, which is trained by proposed fractional calculus based flower pollination optimisation algorithm. FPFCO is used for the effective removal of artefacts from EEG signal. The proposed technique is compared with existing techniques in terms of SNR and MSE.

Findings

The proposed technique is compared with existing techniques in terms of SNR, RMSE and MSE.

Originality/value

100%.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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

Keywords

Article
Publication date: 28 October 2014

Yi-Yeh Lee, Aaron Raymond See, Shih-Chung Chen and Chih-Kuo Liang

– The purpose of this paper was to investigate the response of good and poor sleepers toward audio-visual stimulation via prefrontal theta EEG measurement.

Abstract

Purpose

The purpose of this paper was to investigate the response of good and poor sleepers toward audio-visual stimulation via prefrontal theta EEG measurement.

Design/methodology/approach

The experiment included ten healthy subjects that were chosen after going through the Pittsburgh Sleep Quality Index (PSQI). They were divided into two groups that include five good and five poor sleepers. Next, in order to clarify the effects of audio-visual biofeedback during daytime, each subject was asked to go through six two-minute tasks that include: pre-baseline, eyes open at rest, eyes closed at rest, audio biofeedback with eyes open, video biofeedback also with eyes open, and post-baseline.

Findings

In Task 4, the audio stimulation task, both types of sleepers elicited higher theta waves due to demand in mental activity and also a meditation state. It was significantly higher in poor sleeper that demonstrated a peak difference of 25 percent compared to its good sleeper counterpart. In Task 5, the visual stimulation task, through the use of random numbers having blue and red color background, the theta amplitudes of good and poor sleepers drop together, due to beta waves becoming dominant, as the task required attention and focussed accounting for reduced theta amplitudes. The study was able to prove the use of prefrontal EEG in measuring and evaluating sleep quality by examining theta variation.

Originality/value

This paper proposed a novel and convenient method for evaluating sleep quality by utilizing only a single channel prefrontal EEG measurement.

Details

Engineering Computations, vol. 31 no. 8
Type: Research Article
ISSN: 0264-4401

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: 3 April 2017

Kip Errett Patterson

The purpose of this paper is to present a theory that applies Miller et al.’s (1960) Test-Operate-Test-Exit (TOTE) concept to the psychophysiology involved in…

Abstract

Purpose

The purpose of this paper is to present a theory that applies Miller et al.’s (1960) Test-Operate-Test-Exit (TOTE) concept to the psychophysiology involved in electroencephalographic (EEG) biofeedback (BFB).

Design/methodology/approach

Six components are presented, namely, the teleological brain, attractors as the “test” in TOTEs, EEG production, positive and negative feedback, synaptogenesis and designated actor, and then integrated into a theoretical structure. Comparisons with the previous conceptualizations are discussed, and finally, suggestions for practical application and needed research are offered.

Findings

Previous theories neglected significant variables and promoted unverified conceptualizations. These issues are redressed with a psychophysiological, cybernetic theory.

Research limitations/implications

The pursuit of substantive research needed to verify the theory would improve the scientific foundations for EEG BFB.

Practical implications

This theory shifts the designated actor in BFB to the participant’s brain, away from the BFB provider. EEG BFB is thus viewed as a means for neuronominalization driven by the brain’s attractor systems instead of as an intrusive intervention.

Social implications

The theory proposes a much more participant-centric process than previous modes, which also promotes self-determination. The research validation needed for the theory could produce wider EEG BFB acceptance and application.

Originality/value

The theory is a complete departure from previous conceptualizations. It is the first instance of TOTE application to psychophysiological processes, and it is the first fully cybernetic conceptualization of EEG BFB.

Details

Kybernetes, vol. 46 no. 4
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 29 January 2018

Meng-Hsien (Jenny) Lin, Samantha N.N. Cross, William J. Jones and Terry L. Childers

This paper aims to review past papers focused on understanding consumer-related topics in marketing and related interdisciplinary fields to demonstrate the applications of…

3788

Abstract

Purpose

This paper aims to review past papers focused on understanding consumer-related topics in marketing and related interdisciplinary fields to demonstrate the applications of electroencephalogram (EEG) in consumer neuroscience.

Design/methodology/approach

In addition to the review of papers using EEG to study consumer cognitive processes, the authors also discuss relevant decisions and considerations in conducting event-related potential (ERP) studies. Further, a framework proposed by Plassmann et al. (2015) was used to discuss the applications of EEG in marketing research from papers reviewed.

Findings

This paper successfully used Plassmann et al.’s (2015) framework to discuss five applications of neuroscience to marketing research. A review of growing EEG studies in the field of marketing and other interdisciplinary fields reveals the advantages and potential of using EEG in combination with other methods. This calls for more research using such methods.

Research limitations/implications

A technical overview of ERP-related terminology provides researchers with a background for understanding and reviewing ERP studies. A discussion of method-related considerations and decisions provides marketing researchers with an introduction to the method and refers readers to relevant literature.

Practical implications

The marketing industry has been quick to adopt cutting edge technology, including EEG, to understand and predict consumer behavior for the purpose of improving marketing practices. This paper connects the academic and practitioner spheres by presenting past and potential EEG research that can be translatable to the marketing industry.

Originality/value

The authors review past literature on the use of EEG to study consumer-related topics in marketing and interdisciplinary fields, to demonstrate its advantages over-traditional methods in studying consumer-relevant behaviors. To foster increasing use of EEG in consumer neuroscience research, the authors further provide technical and marketing-specific considerations for both academic and market researchers. This paper is one of the first to review past EEG papers and provide methodological background insights for marketing researchers.

Details

European Journal of Marketing, vol. 52 no. 1/2
Type: Research Article
ISSN: 0309-0566

Keywords

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