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Article
Publication date: 15 June 2020

Electromyography features during physical and imagined standing up in healthy young adults, Phitsanulok, Thailand

Kanokwan Srisupornkornkool, Kanphajee Sornkaew, Kittithat Chatkanjanakool, Chayanit Ampairattana, Pariyanoot Pongtasom, Sompiya Somthavil, Onuma Boonyarom, Kornanong Yuenyongchaiwat and Khajonsak Pongpanit

To compare the electromyography (EMG) features during physical and imagined standing up in healthy young adults.

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Abstract

Purpose

To compare the electromyography (EMG) features during physical and imagined standing up in healthy young adults.

Design/methodology/approach

Twenty-two participants (ages ranged from 20–29 years old) were recruited to participate in this study. Electrodes were attached to the rectus femoris, biceps femoris, tibialis anterior and the medial gastrocnemius muscles of both sides to monitor the EMG features during physical and imagined standing up. The %maximal voluntary contraction (%MVC), onset and duration were calculated.

Findings

The onset and duration of each muscle of both sides had no statistically significant differences between physical and imagined standing up (p > 0.05). The %MVC of all four muscles during physical standing up was statistically significantly higher than during imagined standing up (p < 0.05) on both sides. Moreover, the tibialis anterior muscle of both sides showed a statistically significant contraction before the other muscles (p < 0.05) during physical and imagined standing up.

Originality/value

Muscles can be activated during imagined movement, and the patterns of muscle activity during physical and imagined standing up were similar. Imagined movement may be used in rehabilitation as an alternative or additional technique combined with other techniques to enhance the STS skill.

Details

Journal of Health Research, vol. 35 no. 1
Type: Research Article
DOI: https://doi.org/10.1108/JHR-08-2019-0175
ISSN: 0857-4421

Keywords

  • Electromyography
  • Motor imagery
  • Standing up
  • Thailand

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Article
Publication date: 30 September 2020

Motor imagery-based EEG signals classification by combining temporal and spatial deep characteristics

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…

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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
DOI: https://doi.org/10.1108/IJICC-07-2020-0077
ISSN: 1756-378X

Keywords

  • Convolutional neural network
  • Motor imagery
  • Brain-computer interface
  • EEG signal
  • Temporal and spatial characteristics

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Article
Publication date: 1 September 2003

The use of mental imagery in the simulated employment interview situation

Mike Knudstrup, Sharon L. Segrest and Amy E. Hurley

In this study, interviewees in the training group were instructed to use mental imagery techniques in a simulated employment interview. Results indicated that the subjects…

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Abstract

In this study, interviewees in the training group were instructed to use mental imagery techniques in a simulated employment interview. Results indicated that the subjects who used mental imagery had higher performance in the interview and lower perceived stress than the subjects who did not use mental imagery. Mental imagery did not have a significant effect upon perceptions of self‐efficacy. Mental imagery ability had a positive effect on perceived usefulness of mental imagery while controllability and vividness did not. Subjects did indicate positive perceptions of the mental imagery intervention and a willingness to use mental imagery again in the future. The personality variable, “conscientiousness”, had a significant effect in the mental imagery performance relationship.

Details

Journal of Managerial Psychology, vol. 18 no. 6
Type: Research Article
DOI: https://doi.org/10.1108/02683940310494395
ISSN: 0268-3946

Keywords

  • Interviews
  • Stress
  • Simulation

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Article
Publication date: 1 July 2000

A role for imagery in mentoring

Sarah Fletcher

This article suggests that simple imagery and visualization techniques can be used with the mentoring relationship. After identifying the challenge that mentors need…

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Abstract

This article suggests that simple imagery and visualization techniques can be used with the mentoring relationship. After identifying the challenge that mentors need strategies to promote mentee development, the article presents a case for using visualization and shows how this strategy has been used in other contexts. Visualization and imagery is then applied to pre‐service teachers. The article concludes by exploring the potential for the use of visualization by mentors arguing that visualisation could help bring about self‐actualization.

Details

Career Development International, vol. 5 no. 4/5
Type: Research Article
DOI: https://doi.org/10.1108/EUM0000000005361
ISSN: 1362-0436

Keywords

  • Mentors
  • Development
  • Learning

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Article
Publication date: 11 July 2016

Exploring virtual mental practice in maintenance task training

Tim Bauerle, Michael J. Brnich and Jason Navoyski

This paper aims to contribute to a general understanding of mental practice by investigating the utility of and participant reaction to a virtual reality maintenance…

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Abstract

Purpose

This paper aims to contribute to a general understanding of mental practice by investigating the utility of and participant reaction to a virtual reality maintenance training among underground coal mine first responders.

Design/methodology/approach

Researchers at the National Institute for Occupational Safety and Health’s Office of Mine Safety and Health Research (OMSHR) developed software to provide opportunities for mine rescue team members to learn to inspect, assemble and test their closed-circuit breathing apparatus and to practice those skills. In total, 31 mine rescue team members utilized OMSHR’s BG 4 Benching Trainer software and provided feedback to the development team. After training, participants completed a brief post-training questionnaire, which included demographics, perceived training climate and general training evaluation items.

Findings

The results overall indicate a generally positive reaction to and high perceived utility of the BG 4 benching software. In addition, the perceived training climate appears to have an effect on the perceived utility of the mental practice virtual reality game, with benchmen from mines with more positive training climates reporting greater perceived efficacy in the training’s ability to prepare trainees for real emergencies.

Originality/value

This paper helps to broaden current applications of mental practice and is one of the few empirical investigations into a non-rehabilitation virtual reality extension of mental practice. This paper also contributes to the growing literature advocating for greater usage of accurate and well-informed mental practice techniques, tools and methodologies, especially for occupational populations with limitations on exposure to hands-on training.

Details

Journal of Workplace Learning, vol. 28 no. 5
Type: Research Article
DOI: https://doi.org/10.1108/JWL-10-2015-0078
ISSN: 1366-5626

Keywords

  • Computer based learning
  • Training evaluation
  • Training climate

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Article
Publication date: 18 June 2020

A novel multi-dimensional features fusion algorithm for the EEG signal recognition of brain's sensorimotor region activated tasks

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…

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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
DOI: https://doi.org/10.1108/IJICC-02-2020-0019
ISSN: 1756-378X

Keywords

  • Brain's sensorimotor region activated tasks
  • Brain-computer interface
  • Morlet wavelet
  • 3DCNNs model
  • Bi-GRUs model

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Article
Publication date: 3 December 2020

A dual-arm mobile robot system performing assistive tasks operated via P300-based brain computer interface

Giuseppe Gillini, Paolo Di Lillo, Filippo Arrichiello, Daniele Di Vito, Alessandro Marino, Gianluca Antonelli and Stefano Chiaverini

In the past decade, more than 700 million people are affected by some kind of disability or handicap. In this context, the research interest in assistive robotics is…

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Abstract

Purpose

In the past decade, more than 700 million people are affected by some kind of disability or handicap. In this context, the research interest in assistive robotics is growing up. For people with mobility impairments, daily life operations, as dressing or feeding, require the assistance of dedicated people; thus, the use of devices providing independent mobility can have a large impact on improving their life quality. The purpose of this paper is to present the development of a robotic system aimed at assisting people with this kind of severe motion disabilities by providing a certain level of autonomy.

Design/methodology/approach

The system is based on a hierarchical architecture where, at the top level, the user generates simple and high-level commands by resorting to a graphical user interface operated via a P300-based brain computer interface. These commands are ultimately converted into joint and Cartesian space tasks for the robotic system that are then handled by the robot motion control algorithm resorting to a set-based task priority inverse kinematic strategy. The overall architecture is realized by integrating control and perception software modules developed in the robots and systems environment with the BCI2000 framework, used to operate the brain–computer interfaces (BCI) device.

Findings

The effectiveness of the proposed architecture is validated through experiments where a user generates commands, via an Emotiv Epoc+ BCI, to perform assistive tasks that are executed by a Kinova MOVO robot, i.e. an omnidirectional mobile robotic platform equipped with two lightweight seven degrees of freedoms manipulators.

Originality/value

The P300 paradigm has been successfully integrated with a control architecture that allows us to command a complex robotic system to perform daily life operations. The user defines high-level commands via the BCI, letting all the low-level tasks, for example, safety-related tasks, to be handled by the system in a completely autonomous manner.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
DOI: https://doi.org/10.1108/IR-07-2020-0137
ISSN: 0143-991X

Keywords

  • Control
  • Robotics
  • Man–machine interface (MMI)
  • Autonomous robots

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Article
Publication date: 17 October 2008

Brain‐controlled robot agent: an EEG‐based eRobot agent

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.

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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
DOI: https://doi.org/10.1108/01439910810909501
ISSN: 0143-991X

Keywords

  • Robotics
  • Man machine interface
  • Computer networks
  • Control systems

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Article
Publication date: 21 May 2018

Rehabilitation robots

Robert Bogue

This paper aims to provide an insight into the emerging use of robots in the rehabilitation of sufferers from strokes and other neurological impediments.

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Abstract

Purpose

This paper aims to provide an insight into the emerging use of robots in the rehabilitation of sufferers from strokes and other neurological impediments.

Design/methodology/approach

This considers research, clinical trials and commercial products. Following an introduction, it explains brain neuroplasticity and its role in rehabilitation and then discusses the use of robots in the restoration of upper limb and hand movement in stroke and traumatic injury patients. Robotic techniques aimed at restoring ambulatory ability are then discussed, followed by examples of the application of brain–computer interface technology to robotic rehabilitation. Finally, concluding comments are drawn.

Findings

Research has shown that robotic techniques can assist in the restoration of functionality to partially or fully paralysed upper and lower limbs. A growing number of commercial exoskeleton and end-effector robotic products have been launched which are augmenting conventional rehabilitation therapies. These systems frequently include interactive computer games and tasks which encourage repetitive use and allow patients to monitor their progress. Trials which combine robotics with brain–computer interface technology have yielded encouraging and unexpectedly positive results.

Originality/value

This provides details of the increasingly important role played by robots in the rehabilitation of patients suffering from strokes and other neurological disorders.

Details

Industrial Robot: An International Journal, vol. 45 no. 3
Type: Research Article
DOI: https://doi.org/10.1108/IR-03-2018-0046
ISSN: 0143-991X

Keywords

  • Rehabilitation
  • Robot
  • Exoskeleton
  • Neuroplasticity
  • Brain-computer interface
  • Strokes

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Book part
Publication date: 30 August 2019

A Bayesian Model for Activation and Connectivity in Task-related fMRI Data

Zhe Yu, Raquel Prado, Steve C. Cramer, Erin B. Quinlan and Hernando Ombao

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates…

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Abstract

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

Details

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A
Type: Book
DOI: https://doi.org/10.1108/S0731-90532019000040A006
ISBN: 978-1-78973-241-2

Keywords

  • fMRI
  • activation
  • effective connectivity
  • functional connectivity
  • hemodynamic response function
  • deconvolution
  • Bayesian inference
  • task-related

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