scholarly journals Development of a Guidance System for Motor Imagery Enhancement Using the Virtual Hand Illusion

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2197
Author(s):  
Hojun Jeong ◽  
Jonghyun Kim
Keyword(s):  
Motor Imagery ◽  
Motion Tracking ◽  
Guidance System ◽  
Body Parts ◽  
Physical Constraints ◽  
Virtual Hand ◽  
Computer Interfaces ◽  
Input Signals ◽  
Execution Cycle ◽  
Guidance Systems

Motor imagery (MI) is widely used to produce input signals for brain–computer interfaces (BCI) due to the similarities between MI-BCI and the planning–execution cycle. Despite its usefulness, MI tasks can be ambiguous to users and MI produces weaker cortical signals than motor execution. Existing MI guidance systems, which have been reported to provide visual guidance for MI and enhance MI, still have limitations: insufficient immersion for MI or poor expandability to MI for another body parts. We propose a guidance system for MI enhancement that can immerse users in MI and will be easy to extend to other body parts and target motions with few physical constraints. To make easily extendable MI guidance system, the virtual hand illusion is applied to the MI guidance system with a motion tracking sensor. MI enhancement was evaluated in 11 healthy people by comparison with another guidance system and conventional motor commands for BCI. The results showed that the proposed MI guidance system produced an amplified cortical signal compared to pure MI (p < 0.017), and a similar cortical signal as those produced by both actual execution (p > 0.534) and an MI guidance system with the rubber hand illusion (p > 0.722) in the contralateral region. Therefore, we believe that the proposed MI guidance system with the virtual hand illusion is a viable alternative to existing MI guidance systems in various applications with MI-BCI.

scholarly journals Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

2020 ◽  
Vol 16 (2) ◽  
pp. 236-242
Author(s):  
Muhamad Firdaus Mohd Rafi ◽  
Arief Ruhullah A Harris ◽  
Tan Tian Swee ◽  
Kah Meng Leong ◽  
Jia Hou Tan ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Phase Stability ◽  
Phase Synchronization ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Imagery Task ◽  
Body Parts ◽  
Time Data ◽  
Input Signals ◽  
Human Augmentation

Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a locked-in state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain-Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements.

scholarly journals Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Alkinoos Athanasiou ◽  
Chrysa Lithari ◽  
Konstantina Kalogianni ◽  
Manousos A. Klados ◽  
Panagiotis D. Bamidis
Keyword(s):  
Functional Connectivity ◽  
Motor Imagery ◽  
Sensorimotor Cortex ◽  
Mu Rhythm ◽  
Source Detection ◽  
Motor Execution ◽  
Motor Tasks ◽  
Computer Interfaces ◽  
Preliminary Study ◽  
Real Hand

Introduction. Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas.Materials and Methods. Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome.Results and Discussion. Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution.Conclusions. Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces.

scholarly journals User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

2016 ◽  
Vol 7 ◽  
Author(s):  
Luz M. Alonso-Valerdi ◽  
David A. Gutiérrez-Begovich ◽  
Janet Argüello-García ◽  
Francisco Sepulveda ◽  
Ricardo A. Ramírez-Mendoza
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Motor Imagery ◽  
User Experience ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
System Adaptation

Preliminary experience with Brown-Roberts-Wells (BRW) computerized tomography stereotaxic guidance system

1983 ◽  
Vol 59 (2) ◽  
pp. 217-222 ◽  
Author(s):  
M. Peter Heilbrun ◽  
Theodore S. Roberts ◽  
Michael L. J. Apuzzo ◽  
Trent H. Wells ◽  
James K. Sabshin
Keyword(s):  
Ct Scan ◽  
Computerized Tomography ◽  
Guidance System ◽  
Production Model ◽  
Ct Scanner ◽  
Content Type ◽  
Ct Scanners ◽  
Guidance Systems ◽  
Fine Print

✓ The production model of the Brown-Roberts-Wells (BRW) computerized tomography (CT) stereotaxic guidance system is described. Hardware and software modifications to the original prototype now allow the system to be used independently of the CT scanner after an initial scan with the localizing components fixed to the skull. The system is simple and efficient, can be used universally with all CT scanners, and includes a phantom simulator system for target verification. Preliminary experience with 74 patients at two institutions is described. It is concluded that CT stereotaxic guidance systems will become important tools in the neurosurgical armamentarium, as they allow accurate approach to any target identifiable on the CT scan.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

2021 ◽  
pp. 2150030
Author(s):  
Jing Jin ◽  
Hua Fang ◽  
Ian Daly ◽  
Ruocheng Xiao ◽  
Yangyang Miao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Performance ◽  
Minimum Covariance Determinant ◽  
Fisher Score ◽  
Practical Applications ◽  
Computer Interfaces ◽  
Highly Sensitive ◽  
Feature Optimization ◽  
The Common ◽  
Model Training

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain–computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

scholarly journals What if I had a third arm? An EEG study of a supernumerary BCI system

2019 ◽  
Author(s):  
Jaime A. Riascos ◽  
David Steeven Villa ◽  
Anderson Maciel ◽  
Luciana Nedel ◽  
Dante Barone
Keyword(s):  
Motor Imagery ◽  
Body Parts ◽  
Task Load ◽  
The Third ◽  
Person Perspective ◽  
Human Ability ◽  
Robotic Prosthesis ◽  
And Control ◽  
Bci System ◽  
Psychological Experiments

AbstractMotor imagery Brain-Computer Interface (MI-BCI) enables bodyless communication by means of the imagination of body movements. Since its apparition, MI-BCI has been widely used in applications such as guiding a robotic prosthesis, or the navigation in games and virtual reality (VR) environments. Although psychological experiments, such as the Rubber Hand Illusion - RHI, suggest the human ability for creating body transfer illusions, MI-BCI only uses the imagination of real body parts as neurofeedback training and control commands. The present work studies and explores the inclusion of an imaginary third arm as a part of the control commands for MI-BCI systems. It also compares the effectiveness of using the conventional arrows and fixation cross as training step (Graz condition) against realistic human hands performing the corresponding tasks from a first-person perspective (Hands condition); both conditions wearing a VR headset. Ten healthy subjects participated in a two-session EEG experiment involving open-close hand tasks, including a third arm that comes out from the chest. The EEG analysis shows a strong power decrease in the sensory-motor areas for the third arm task in both training conditions. Such activity is significantly stronger for Hands than Graz condition, suggesting that the realistic scenario can reduce the abstractness of the third arm and improve the generation of motor imagery signals. The cognitive load is also assessed both by NASA-TLX and Task Load index.

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