scholarly journals Influence of the visuo-proprioceptive illusion of movement and motor imagery of the wrist on EEG cortical excitability among healthy participants

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256723
Author(s):  
Salomé Le Franc ◽  
Mathis Fleury ◽  
Camille Jeunet ◽  
Simon Butet ◽  
Christian Barillot ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Cortical Excitability ◽  
Tendon Vibration ◽  
Motor Rehabilitation ◽  
Eeg Signals ◽  
Brain Areas ◽  
Significant Difference ◽  
The Brain ◽  
Healthy Participants ◽  
Motor Areas

Introduction Motor Imagery (MI) is a powerful tool to stimulate sensorimotor brain areas and is currently used in motor rehabilitation after a stroke. The aim of our study was to evaluate whether an illusion of movement induced by visuo-proprioceptive immersion (VPI) including tendon vibration (TV) and Virtual moving hand (VR) combined with MI tasks could be more efficient than VPI alone or MI alone on cortical excitability assessed using Electroencephalography (EEG). Methods We recorded EEG signals in 20 healthy participants in 3 different conditions: MI tasks involving their non-dominant wrist (MI condition); VPI condition; and VPI with MI tasks (combined condition). Each condition lasted 3 minutes, and was repeated 3 times in randomized order. Our main judgment criterion was the Event-Related De-synchronization (ERD) threshold in sensori-motor areas in each condition in the brain motor area. Results The combined condition induced a greater change in the ERD percentage than the MI condition alone, but no significant difference was found between the combined and the VPI condition (p = 0.07) and between the VPI and MI condition (p = 0.20). Conclusion This study demonstrated the interest of using a visuo-proprioceptive immersion with MI rather than MI alone in order to increase excitability in motor areas of the brain. Further studies could test this hypothesis among patients with stroke to provide new perspectives for motor rehabilitation in this population.

scholarly journals Visual feedback improves movement illusions induced by tendon vibration after chronic stroke

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Salomé Le Franc ◽  
Isabelle Bonan ◽  
Mathis Fleury ◽  
Simon Butet ◽  
Christian Barillot ◽  
...  
Keyword(s):  
Visual Cues ◽  
Static Condition ◽  
Chronic Stroke ◽  
Clinical Severity ◽  
Tendon Vibration ◽  
Motor Rehabilitation ◽  
Virtual Hand ◽  
Significant Difference ◽  
Illusory Movement ◽  
The Brain

Abstract Background Illusion of movement induced by tendon vibration is commonly used in rehabilitation and seems valuable for motor rehabilitation after stroke, by playing a role in cerebral plasticity. The aim was to study if congruent visual cues using Virtual Reality (VR) could enhance the illusion of movement induced by tendon vibration of the wrist among participants with stroke. Methods We included 20 chronic stroke participants. They experienced tendon vibration of their wrist (100 Hz, 30 times) inducing illusion of movement. Three VR visual conditions were added to the vibration: a congruent moving virtual hand (Moving condition); a static virtual hand (Static condition); or no virtual hand at all (Hidden condition). The participants evaluated for each visual condition the intensity of the illusory movement using a Likert scale, the sensation of wrist’s movement using a degree scale and they answered a questionnaire about their preferred condition. Results The Moving condition was significantly superior to the Hidden condition and to the Static condition in terms of illusion of movement (p < 0.001) and the wrist’s extension (p < 0.001). There was no significant difference between the Hidden and the Static condition for these 2 criteria. The Moving condition was considered the best one to increase the illusion of movement (in 70% of the participants). Two participants did not feel any illusion of movement. Conclusions This study showed the interest of using congruent cues in VR in order to enhance the consistency of the illusion of movement induced by tendon vibration among participants after stroke, regardless of their clinical severity. By stimulating the brain motor areas, this visuo-proprioceptive feedback could be an interesting tool in motor rehabilitation. Record number in Clinical Trials: NCT04130711, registered on October 17th 2019 (https://clinicaltrials.gov/ct2/show/NCT04130711?id=NCT04130711&draw=2&rank=1).

scholarly journals Influence of virtual reality visual feedback on the illusion of movement induced by tendon vibration of wrist in healthy participants

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242416
Author(s):  
Salomé Le Franc ◽  
Mathis Fleury ◽  
Mélanie Cogne ◽  
Simon Butet ◽  
Christian Barillot ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Visual Feedback ◽  
Visual Cues ◽  
Static Condition ◽  
Likert Scale ◽  
Tendon Vibration ◽  
Virtual Hand ◽  
Significant Difference ◽  
Illusory Movement ◽  
Healthy Participants

Introduction Illusion of movement induced by tendon vibration is an effective approach for motor and sensory rehabilitation in case of neurological impairments. The aim of our study was to investigate which modality of visual feedback in Virtual Reality (VR) associated with tendon vibration of the wrist could induce the best illusion of movement. Methods We included 30 healthy participants in the experiment. Tendon vibration inducing illusion of movement (wrist extension, 100Hz) was applied on their wrist during 3 VR visual conditions (10 times each): a moving virtual hand corresponding to the movement that the participants could feel during the tendon vibration (Moving condition), a static virtual hand (Static condition), or no virtual hand at all (Hidden condition). After each trial, the participants had to quantify the intensity of the illusory movement on a Likert scale, the subjective degree of extension of their wrist and afterwards they answered a questionnaire. Results There was a significant difference between the 3 visual feedback conditions concerning the Likert scale ranking and the degree of wrist’s extension (p<0.001). The Moving condition induced a higher intensity of illusion of movement and a higher sensation of wrist’s extension than the Hidden condition (p<0.001 and p<0.001 respectively) than that of the Static condition (p<0.001 and p<0.001 respectively). The Hidden condition also induced a higher intensity of illusion of movement and a higher sensation of wrist’s extension than the Static condition (p<0.01 and p<0.01 respectively). The preferred condition to facilitate movement’s illusion was the Moving condition (63.3%). Conclusions This study demonstrated the importance of carefully selecting a visual feedback to improve the illusion of movement induced by tendon vibration, and the increase of illusion by adding VR visual cues congruent to the illusion of movement. Further work will consist in testing the same hypothesis with stroke patients.

scholarly journals Detection of Human Brain Activities in a Normal Human being

2019 ◽  
Vol 8 (10) ◽  
pp. 4004-4007
Keyword(s):  
Human Brain ◽  
Motor Imagery ◽  
Crucial Role ◽  
Software Tool ◽  
Eeg Signals ◽  
Eye Blink ◽  
Eye Blinks ◽  
Eeg Data ◽  
Normal Human ◽  
The Brain

Detection of artifacts produced in EEG data by eye blinks is a very common problem in EEG research. In this paper we address the detection of eye blink artifacts in a motor imagery (MI) EEG data. Artifacts are nothing but some kind of disturbances present in the brain signal whose origin is not the brain itself. Detection of unwanted artifacts plays a crucial role to acquire artifact free and clean brain EEG signals to analyze and detect brain activities. There are generally two ways of generation of artifacts. From a recorded signal most common and important artifacts in the form of eye blinks are recognized and encapsulated. In this paper a new software tool named BRAINSTORM is introduced for the detection of eye blink artifacts.

DECODING OF SIMPLE AND COMPOUND LIMB MOTOR IMAGERY MOVEMENTS BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950041 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
TIRDAD SEIFI ALA
Keyword(s):  
Fractal Dimension ◽  
Motor Imagery ◽  
Fractal Analysis ◽  
Brain Activity ◽  
Eeg Signal ◽  
Left Hand ◽  
Right Hand ◽  
Significant Difference ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the major attempts in rehabilitation science is to decode different movements of human using physiological signals. Since human movements are mainly controlled by the brain, decoding of movements by analysis of the brain activity has great importance. In this paper, we apply fractal analysis to Electroencephalogram (EEG) signal in order to decode simple and compound limb motor imagery movements. The fractal dimension of EEG signal is analyzed in case of left hand, right hand, both hands, feet, left hand combined with right foot, and right hand combined with left foot movements. Based on the obtained results, EEG signal experiences the lowest and greatest fractal dimension in case of both hands movement, and feet movement, respectively. Besides obtaining different fractal dimension for EEG signal in case of different movements, no significant difference was observed in fractal dimension of EEG signal between different movements. The method of analysis employed in this research can be widely applied to analysis of EEG signal for decoding of different movements of human.

Emulation of kinesthesia during motor imagery

2004 ◽  
Vol 27 (3) ◽  
pp. 412-413 ◽  
Author(s):  
Norihiro Sadato ◽  
Eiichi Naito
Keyword(s):  
Motor Imagery ◽  
Supplementary Motor Area ◽  
Premotor Cortex ◽  
Motor Area ◽  
Tendon Vibration ◽  
Dorsal Premotor Cortex ◽  
Mental Rehearsal ◽  
Cingulate Motor Area ◽  
Motor Areas

Illusory kinesthetic sensation was influenced by motor imagery of the wrist following tendon vibration. The imagery and the illusion conditions commonly activated the contralateral cingulate motor area, supplementary motor area, dorsal premotor cortex, and ipsilateral cerebellum. This supports the notion that motor imagery is a mental rehearsal of movement, during which expected kinesthetic sensation is emulated by recruiting multiple motor areas, commonly activated by pure kinesthesia.

scholarly journals Investigation of EEG signals generated by motor imagery for application in BCIs

2018 ◽  
Author(s):  
Caique de Medeiros Mendes ◽  
Gabriela Castellano ◽  
Carlos Alberto Stefano Filho
Keyword(s):  
Motor Imagery ◽  
Low Complexity ◽  
Eeg Signals ◽  
Brain Response ◽  
K Nearest Neighbors ◽  
Computer Interfaces ◽  
Power Spectral ◽  
Motor Movements ◽  
Accuracy Rates ◽  
The Brain

Motor imagery (MI) is a commonly used strategy in brain-computer interfaces (BCIs) to modify neuronal activity, in which the user, by imagining motor movements, generates signals that can be recorded and interpreted to control a device. In this study, we sought to investigate how the brain response of users during MI happens, by analyzing a database of EEG signals in which healthy subjects were asked to imagine the movement of their right and left hands. Our goal was to recognize patterns associated with this task, through a spectral evaluation of different segments of the signal. Therefore, we estimated the power spectral density (PSD) for each evaluated segment and then used it for classification, via k-nearest neighbors (k-NN). We found that the accuracy rates obtained with k-NN classification were very similar to random, suggesting, mainly, high inter-subjects variability and choice of a low complexity classifier.

AUTOMATED DIAGNOSIS OF NORMAL AND ALCOHOLIC EEG SIGNALS

2012 ◽  
Vol 22 (03) ◽  
pp. 1250011 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
S. VINITHA SREE ◽  
SUBHAGATA CHATTOPADHYAY ◽  
JASJIT S. SURI
Keyword(s):  
Surrogate Data ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Frequency Domain Methods ◽  
Significant Difference ◽  
Nonlinear Features ◽  
The Brain

Electroencephalogram (EEG) signals, which record the electrical activity in the brain, are useful for assessing the mental state of a person. Since these signals are nonlinear and non-stationary in nature, it is very difficult to decipher the useful information from them using conventional statistical and frequency domain methods. Hence, the application of nonlinear time series analysis to EEG signals could be useful to study the dynamical nature and variability of the brain signals. In this paper, we propose a Computer Aided Diagnostic (CAD) technique for the automated identification of normal and alcoholic EEG signals using nonlinear features. We first extract nonlinear features such as Approximate Entropy (ApEn), Largest Lyapunov Exponent (LLE), Sample Entropy (SampEn), and four other Higher Order Spectra (HOS) features, and then use them to train Support Vector Machine (SVM) classifier of varying kernel functions: 1st, 2nd, and 3rd order polynomials and a Radial basis function (RBF) kernel. Our results indicate that these nonlinear measures are good discriminators of normal and alcoholic EEG signals. The SVM classifier with a polynomial kernel of order 1 could distinguish the two classes with an accuracy of 91.7%, sensitivity of 90% and specificity of 93.3%. As a pre-analysis step, the EEG signals were tested for nonlinearity using surrogate data analysis and we found that there was a significant difference in the LLE measure of the actual data and the surrogate data.

scholarly journals Motor Imagery Training After Stroke Increases Slow-5 Oscillations and Functional Connectivity in the Ipsilesional Inferior Parietal Lobule

2020 ◽  
Vol 34 (4) ◽  
pp. 321-332 ◽  
Author(s):  
Xu Wang ◽  
Hewei Wang ◽  
Xin Xiong ◽  
Changhui Sun ◽  
Bing Zhu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Motor Imagery ◽  
Resting State ◽  
Control Group ◽  
Inferior Parietal Lobule ◽  
Brain Areas ◽  
Whole Brain ◽  
Imagery Training ◽  
Parietal Lobule ◽  
Motor Areas

Background. Reorganization in motor areas have been suggested after motor imagery training (MIT). However, motor imagery involves a large-scale brain network, in which many regions, andnot only the motor areas, potentially constitute the neural substrate for MIT. Objective. This study aimed to identify the targets for MIT in stroke rehabilitation from a voxel-based whole brain analysis of resting-state functional magnetic resonance imaging (fMRI). Methods. Thirty-four chronic stroke patients were recruited and randomly assigned to either an MIT group or a control group. The MIT group received a 4-week treatment of MIT plus conventional rehabilitation therapy (CRT), whereas the control group only received CRT. Before and after intervention, the Fugl-Meyer Assessment Upper Limb subscale (FM-UL) and resting-state fMRI were collected. The fractional amplitude of low-frequency fluctuations (fALFF) in the slow-5 band (0.01-0.027 Hz) was calculated across the whole brain to identify brain areas with distinct changes between 2 groups. These brain areas were then targeted as seeds to perform seed-based functional connectivity (FC) analysis. Results. In comparison with the control group, the MIT group exhibited more improvements in FM-UL and increased slow-5 fALFF in the ipsilesional inferior parietal lobule (IPL). The change of the slow-5 oscillations in the ipsilesional IPL was positively correlated with the improvement of FM-UL. The MIT group also showed distinct alternations in FCs of the ipsilesional IPL, which were correlated with the improvement of FM-UL. Conclusions. The rehabilitation efficiency of MIT was associated with increased slow-5 oscillations and altered FC in the ipsilesional IPL. Clinical Trial Registration. http://www.chictr.org.cn . Unique Identifier. ChiCTR-TRC-08003005.

Symmetrical feature for interpreting motor imagery EEG signals in the brain–computer interface

Optik ◽  
2017 ◽  
Vol 129 ◽  
pp. 163-171 ◽  
Author(s):  
Seung-Min Park ◽  
Xinyang Yu ◽  
Pharino Chum ◽  
Woo-Young Lee ◽  
Kwee-Bo Sim
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
The Brain
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