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 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.

Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces

2019 ◽  
Vol 57 (8) ◽  
pp. 1709-1725 ◽  
Author(s):  
Paula G. Rodrigues ◽  
Carlos A. Stefano Filho ◽  
Romis Attux ◽  
Gabriela Castellano ◽  
Diogo C. Soriano
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Motor Imagery ◽  
Space Time ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1746
Author(s):  
Laura Ferrero ◽  
Mario Ortiz ◽  
Vicente Quiles ◽  
Eduardo Iáñez ◽  
José A. Flores ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Lower Limb ◽  
Brain Activity ◽  
Control Methods ◽  
Dominant Hemisphere ◽  
Cortical Function ◽  
Lower Limb Exoskeleton ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Sensorimotor Rhythms

Brain–Computer Interfaces (BCI) are systems that allow external devices to be controlled by means of brain activity. There are different such technologies, and electroencephalography (EEG) is an example. One of the most common EEG control methods is based on detecting changes in sensorimotor rhythms (SMRs) during motor imagery (MI). The aim of this study was to assess the laterality of cortical function when performing MI of the lower limb. Brain signals from five subjects were analyzed in two conditions, during exoskeleton-assisted gait and while static. Three different EEG electrode configurations were evaluated: covering both hemispheres, covering the non-dominant hemisphere and covering the dominant hemisphere. In addition, the evolution of performance and laterality with practice was assessed. Although sightly superior results were achieved with information from all electrodes, differences between electrode configurations were not statistically significant. Regarding the evolution during the experimental sessions, the performance of the BCI generally evolved positively the higher the experience was.

scholarly journals The Benefits of Music Listening for Induced State Anxiety: Behavioral and Physiological Evidence

2021 ◽  
Vol 11 (10) ◽  
pp. 1332
Author(s):  
Binxin Huang ◽  
Xiaoting Hao ◽  
Siyu Long ◽  
Rui Ding ◽  
Junce Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
State Anxiety ◽  
Deep Understanding ◽  
Occipital Lobe ◽  
Music Listening ◽  
Clinical Effects ◽  
Effective Interventions ◽  
Power Spectral ◽  
Brain Functional Connectivity ◽  
The Brain

Background: Some clinical studies have indicated that neutral and happy music may relieve state anxiety. However, the brain mechanisms by which these effective interventions in music impact state anxiety remain unknown. Methods: In this study, we selected music with clinical effects for therapy, and 62 subjects were included using the evoked anxiety paradigm. After evoking anxiety with a visual stimulus, all subjects were randomly divided into three groups (listening to happy music, neutral music and a blank stimulus), and EEG signals were acquired. Results: We found that different emotional types of music might have different mechanisms in state anxiety interventions. Neutral music had the effect of alleviating state anxiety. The brain mechanisms supported that neutral music ameliorating state anxiety was associated with decreased power spectral density of the occipital lobe and increased brain functional connectivity between the occipital lobe and frontal lobe. Happy music also had the effect of alleviating state anxiety, and the brain mechanism was associated with enhanced brain functional connectivity between the occipital lobe and right temporal lobe. Conclusions: This study may be important for a deep understanding of the mechanisms associated with state anxiety music interventions and may further contribute to future clinical treatment using nonpharmaceutical interventions.

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.

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.

scholarly journals Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

2021 ◽  
Vol 11 (24) ◽  
pp. 11876
Author(s):  
Catalin Dumitrescu ◽  
Ilona-Madalina Costea ◽  
Augustin Semenescu
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Rapid Change ◽  
Training Session ◽  
Extraction Methods ◽  
Computer Interface ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Sensorimotor Rhythms

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks.

Sign in / Sign up

Export Citation Format

Share Document