scholarly journals Effect of a Brain–Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2020
Author(s):  
Vivianne Flávia Cardoso ◽  
Denis Delisle-Rodriguez ◽  
Maria Alejandra Romero-Laiseca ◽  
Flávia A. Loterio ◽  
Dharmendra Gurve ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Primary Motor Cortex ◽  
Cortical Excitability ◽  
Brain Connectivity ◽  
Sources Of Information ◽  
Beta Band ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Sensory Motor ◽  
On Line

Recently, studies on cycling-based brain–computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.

A Systematic Review on Motor-Imagery Brain-Connectivity-Based Computer Interfaces

2021 ◽  
pp. 1-9
Author(s):  
Lorenza Brusini ◽  
Francesca Stival ◽  
Francesco Setti ◽  
Emanuele Menegatti ◽  
Gloria Menegaz ◽  
...  
Keyword(s):  
Systematic Review ◽  
Motor Imagery ◽  
Brain Connectivity ◽  
Computer Interfaces

scholarly journals Effects of healthy ageing on activation pattern within the brain during movement and motor imagery: an fMRI study

2019 ◽  
Author(s):  
Sanjay Budhdeo ◽  
Jean-Claude Baron ◽  
Nikhil Sharma
Keyword(s):  
Motor Imagery ◽  
Primary Motor Cortex ◽  
Block Design ◽  
Improve Performance ◽  
Computer Interfaces ◽  
Fmri Study ◽  
Age Dependent ◽  
The Neural Networks ◽  
Shared Networks ◽  
The Brain

AbstractIntroductionMotor imagery (MI) has potential as an intervention to improve performance in neurological disease affecting the motor system and to modulate brain computer interfaces (BCI). We hypothesized that the shared networks of MI and executed movement (EM) would be affected by age. Understanding these changes is important in application of MI in neurological disorders.MethodsUsing tensor-independent component analysis (TICA), we mapped the neural networks involved during MI and EM in 31 healthy volunteers (ages 20-72), who were recruited and screened for their ability to perform imagery. We used an fMRI block-design with MI & rest and EM & rest.ResultsTICA defined 37 independent components (ICs). Eight remained after excluding ICs representing artifacts. These ICs accounted for 35% of variance. While all ICs had greater activation in EM than MI. Two ICs increased with greater age for EM only. These ICs contained a bilateral network of brain areas, including primary motor cortex and cerebellum.ConclusionThis study demonstrates the prominence of shared cerebral networks between MI and EM. There are age-dependent changes to EM activation, while MI activation appeared age independent. This strengthens the rationale for using MI to access the motor networks independent of age.

scholarly journals Differences between motor execution and motor imagery of grasping movements in the motor cortical excitatory circuit

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5588 ◽  
Author(s):  
Hai-Jiang Meng ◽  
Yan-Ling Pi ◽  
Ke Liu ◽  
Na Cao ◽  
Yan-Qiu Wang ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Motor Imagery ◽  
Primary Motor Cortex ◽  
Cortical Excitability ◽  
Short Interval ◽  
Corticospinal Excitability ◽  
Motor Execution ◽  
Human Motor Cortex ◽  
Motor Cortical ◽  
The Right

Background Both motor imagery (MI) and motor execution (ME) can facilitate motor cortical excitability. Although cortical excitability is modulated by intracortical inhibitory and excitatory circuits in the human primary motor cortex, it is not clear which intracortical circuits determine the differences in corticospinal excitability between ME and MI. Methods We recruited 10 young healthy subjects aged 18−28 years (mean age: 22.1 ± 3.14 years; five women and five men) for this study. The experiment consisted of two sets of tasks involving grasp actions of the right hand: imagining and executing them. Corticospinal excitability and short-interval intracortical inhibition (SICI) were measured before the interventional protocol using transcranial magnetic stimulation (baseline), as well as at 0, 20, and 40 min (T0, T20, and T40) thereafter. Results Facilitation of corticospinal excitability was significantly greater after ME than after MI in the right abductor pollicis brevis (APB) at T0 and T20 (p < 0.01 for T0, and p < 0.05 for T20), but not in the first dorsal interosseous (FDI) muscle. On the other hand, no significant differences in SICI between ME and MI were found in the APB and FDI muscles. The facilitation of corticospinal excitability at T20 after MI correlated with the Movement Imagery Questionnaire (MIQ) scores for kinesthetic items (Rho = −0.646, p = 0.044) but did not correlate with the MIQ scores for visual items (Rho = −0.265, p = 0.458). Discussion The present results revealed significant differences between ME and MI on intracortical excitatory circuits of the human motor cortex, suggesting that cortical excitability differences between ME and MI may be attributed to the activation differences of the excitatory circuits in the primary motor cortex.

Potentiation of Short-Latency Cortical Responses by High-Frequency Repetitive Transcranial Magnetic Stimulation

2010 ◽  
Vol 104 (3) ◽  
pp. 1578-1588 ◽  
Author(s):  
Domenica Veniero ◽  
Claudio Maioli ◽  
Carlo Miniussi
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
High Frequency ◽  
Magnetic Stimulation ◽  
Primary Motor Cortex ◽  
Cortical Excitability ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Indirect Evidence ◽  
Direct Demonstration ◽  
Resting Motor Threshold ◽  
On Line

It is generally accepted that low- and high-frequency repetitive transcranial magnetic stimulation (rTMS) induces changes in cortical excitability, but there is only indirect evidence of its effects despite a large number of studies employing different stimulation parameters. Typically the cortical modulations are inferred through indirect measurements, such as recording the change in electromyographic responses. Recently it has become possible to directly evaluate rTMS-induced changes at the cortical level using electronencephalography (EEG). The present study investigates the modulation induced by high-frequency rTMS via EEG by evaluating changes in the latency and amplitude of TMS-evoked responses. In this study, rTMS was applied to the left primary motor cortex (MI) in 16 participants while an EEG was simultaneously acquired from 29 scalp electrodes. The rTMS consisted of 40 trains at 20 Hz with 10 stimuli each (a total of 400 stimuli) that were delivered at the individual resting motor threshold. The on-line modulation induced by the high-frequency TMS was characterized by a sequence of EEG responses. Two of the rTMS-induced responses, P5 and N8, were specifically modulated according to the protocol. Their latency decreased from the first to the last TMS stimuli, while the amplitude values increased. These results provide the first direct, on-line evaluation of the effects of high-frequency TMS on EEG activity. In addition, the results provide a direct demonstration of cortical potentiation induced by rTMS in humans.

scholarly journals Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6570
Author(s):  
Muhammad Ahsan Awais ◽  
Mohd Zuki Yusoff ◽  
Danish M. Khan ◽  
Norashikin Yahya ◽  
Nidal Kamel ◽  
...  
Keyword(s):  
Neural Network ◽  
Motor Imagery ◽  
Error Rate ◽  
Classification Accuracy ◽  
Probabilistic Neural Network ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

Motor imagery (MI)-based brain–computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain’s neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms—an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain–computer interfaces.

scholarly journals EEG sensorimotor rhythms’ variation and functional connectivity measures during motor imagery: linear relations and classification approaches

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3983 ◽  
Author(s):  
Carlos A. Stefano Filho ◽  
Romis Attux ◽  
Gabriela Castellano
Keyword(s):  
Functional Connectivity ◽  
Motor Imagery ◽  
Beta Band ◽  
Graph Method ◽  
Graph Metrics ◽  
Computer Interfaces ◽  
Power Spectral ◽  
Careful Exploration ◽  
Brain Functional Connectivity ◽  
Sensorimotor Rhythms

Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands’ power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that information may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any linear correlation between variations in the synchronization patterns—that is, variations in the PSD of mu and beta bands—induced by MI and alterations in the corresponding functional networks. Moreover, we (1) explored the feasibility of using functional connectivity parameters as features for a classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations (p < 0.05) with values ranging from 0.4 to 0.9 between PSD variations and functional network alterations for some electrodes, prominently in the beta band. The PSD method performed better for data classification, with mean accuracies of (90 ± 8)% and (87 ± 7)% for the mu and beta band, respectively, versus (83 ± 8)% and (83 ± 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.

scholarly journals Motor imagery practice during arm-immobilization benefits sensorimotor cortical functions and plasticity-related sleep features

2019 ◽  
Author(s):  
Ursula Debarnot ◽  
Aurore. A. Perrault ◽  
Virginie Sterpenich ◽  
Guillaume Legendre ◽  
Chieko Huber ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Motor Imagery ◽  
Rem Sleep ◽  
Primary Motor Cortex ◽  
Cortical Excitability ◽  
Neural Mechanisms ◽  
Sleep Spindles ◽  
Cortical Representations ◽  
Cortical Functions

ABSTRACTMotor imagery (MI) is known to engage motor networks and could compensate for the maladaptive neuroplasticity elicited by immobilization. This hypothesis and associated underlying neural mechanisms remain underexplored. Here, we investigated how MI practice during 11 h of arm-immobilization influences sensorimotor and cortical representations of the hands, as well as sleep. Fourteen participants were first tested after a normal day, followed by two 11-h periods of immobilization, either with concomitant MI treatment or control tasks. Data revealed that MI prevented the consequences of immobilization: (i) alteration of the sensorimotor representation of hands, (ii) decrease of cortical excitability over the primary motor cortex (M1) contralateral to arm-immobilization, and (iii) reduction of sleep spindles over both M1s. Furthermore, (iv) the time spent in REM sleep was significantly longer after MI. These results support that implementing MI during immobilization can limit the deleterious effects of limb disuse, at several levels of sensorimotor functioning.

scholarly journals Towards ideal time window for classifying motor imagery in brain-computer interfaces

2020 ◽  
Author(s):  
Vitor Mendes Vilas-Boas ◽  
Vitor Da Silva Jorge ◽  
Cleison Daniel Silva
Keyword(s):  
Motor Imagery ◽  
Time Window ◽  
Brain Activity ◽  
Classification Performance ◽  
Brain Computer Interfaces ◽  
Temporal Properties ◽  
Computer Interfaces ◽  
Motor Intention ◽  
Eeg Data ◽  
The Individual

Brain-Computer Interfaces (ICM) allow the control of devices by modulating brain activity. Commonly, when based on motor imagery (IM) these systems use the energy (de)synchronization in the electroencephalogram signal (EEG), voluntarily caused by the individual, to identify and classify their motor intention. Therefore, the EEG segment used in the training of the learning algorithms plays a fundamental role in the description of the characteristics and, consequently, in the recognition of patterns in the signal. In this context, the objective of this work is to demonstrate the correlation between the temporal properties of the input EEG segment and the classification performance of a ICM-IM system. An auxiliary sliding window was used in order to obtain the variation of performance in function of the variation in the time and to support the decision making about the appropriate window. Simulations based on public EEG data point to significant variability in the location and width of the ideal window and suggest the need for individualized selection according to the cognitive patterns of each subject.

scholarly journals Optimal Combination of Anodal Transcranial Direct Current Stimulations and Motor Imagery Interventions

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Elodie Saruco ◽  
Franck Di Rienzo ◽  
Susana Nunez-Nagy ◽  
Miguel A. Rubio-Gonzalez ◽  
Ursula Debarnot ◽  
...  
Keyword(s):  
Postural Control ◽  
Motor Imagery ◽  
Direct Current ◽  
Primary Motor Cortex ◽  
Cortical Excitability ◽  
Homeostatic Plasticity ◽  
Experimental Conditions ◽  
Beneficial Effects ◽  
Imagery Training ◽  
Before And After

Motor imagery contributes to enhance the (re)learning of motor skills through remapping of cortical networks. Combining motor imagery with anodal transcranial direct-current stimulation (a-tDCS) over the primary motor cortex has further been shown to promote its beneficial effects on postural control. Whether motor imagery should be performed concomitantly to a-tDCS (over depolarized membrane) or consecutively (over changing neurotransmitters activity) remains to be elucidated. In the present study, we measured the performance in a postural control task before and after three experimental conditions. Participants received a-tDCS before (tDCSBefore), during (tDCSDuring), or both before and during motor imagery training (tDCSBefore + During). Performance was improved after tDCSDuring, but not after both the tDCSBefore and tDCSBefore + During conditions. These results support that homeostatic plasticity is likely to operate following a-tDCS through decreasing cortical excitability and that motor imagery should be performed during anodal stimulation for optimum gains.

scholarly journals Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5436
Author(s):  
Kyungho Won ◽  
Moonyoung Kwon ◽  
Minkyu Ahn ◽  
Sung Chan Jun
Keyword(s):  
Motor Imagery ◽  
Comparative Performance ◽  
Computer Interfaces ◽  
Model Generalization ◽  
Improve Model ◽  
Eeg Data ◽  
Subject Variability ◽  
Pooling Strategies ◽  
Pooling Strategy ◽  
Electroencephalogram Eeg

Brain–computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

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