Brain–Computer Interface Based on Motor Imagery: The Most Relevant Sources of Electrical Brain Activity

2013 ◽  
pp. 153-163 ◽  
Author(s):  
Alexander A. Frolov ◽  
Dušan Húsek ◽  
Václav Snášel ◽  
Pavel Bobrov ◽  
Olesya Mokienko ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrical Brain Activity

scholarly journals Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

2019 ◽  
Author(s):  
Jennifer Stiso ◽  
Marie-Constance Corsi ◽  
Javier Omar Garcia ◽  
Jean M Vettel ◽  
Fabrizio De Vico Fallani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Distributed Networks ◽  
Motor Dysfunction ◽  
Computer Interface ◽  
Specific Method ◽  
Temporal Expression ◽  
And Behavior ◽  
Brain Behavior

Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method -- non-negative matrix factorization -- to simultaneously identify regularized, covarying subgraphs of functional connectivity and behavior, and to detect the time-varying expression of each subgraph. We find that learning is marked by distributed brain-behavior relations: swifter learners displayed many subgraphs whose temporal expression tracked performance. Learners also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in networks important for sustaining attention. After formalizing the model in the framework of network control theory, we test the model and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

Electrophysiological brain activity during the control of a motor imagery-based brain–computer interface

2017 ◽  
Vol 43 (5) ◽  
pp. 501-511 ◽  
Author(s):  
A. A. Frolov ◽  
G. A. Aziatskaya ◽  
P. D. Bobrov ◽  
R. Kh. Luykmanov ◽  
I. R. Fedotova ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface

Discriminative Learning of Propagation and Spatial Pattern for Motor Imagery EEG Analysis

2013 ◽  
Vol 25 (10) ◽  
pp. 2709-2733 ◽  
Author(s):  
Xinyang Li ◽  
Haihong Zhang ◽  
Cuntai Guan ◽  
Sim Heng Ong ◽  
Kai Keng Ang ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Statistical Significance ◽  
Activity Patterns ◽  
Signal Propagation ◽  
Brain Regions ◽  
Computer Interface ◽  
Scalp Eeg

Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.

scholarly journals Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Chu Kiong Loo ◽  
Andrews Samraj ◽  
Gin Chong Lee
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Estimation Methods ◽  
Support Vector ◽  
Linear Discriminant

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method.

scholarly journals On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4452 ◽  
Author(s):  
José-Vicente Riquelme-Ros ◽  
Germán Rodríguez-Bermúdez ◽  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
José-María Molina-García-Pardo
Keyword(s):  
Motor Imagery ◽  
Brain Plasticity ◽  
Brain Computer Interface ◽  
Musical Training ◽  
Machine Learning Algorithms ◽  
Computer Interface ◽  
Control Group ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Electrical Brain Activity

Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users’ previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems.

Combination of Brain-Computer Interface Training and Goal-Directed Physical Therapy in Chronic Stroke: A Case Report

2010 ◽  
Vol 24 (7) ◽  
pp. 674-679 ◽  
Author(s):  
Doris Broetz ◽  
Christoph Braun ◽  
Cornelia Weber ◽  
Surjo R. Soekadar ◽  
Andrea Caria ◽  
...  
Keyword(s):  
Physical Therapy ◽  
Motor Function ◽  
Goal Attainment ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Chronic Stroke ◽  
Computer Interface ◽  
Motor Abilities ◽  
Electrical Brain Activity ◽  
Wolf Motor Function Test

Background. There is no accepted and efficient rehabilitation strategy to reduce focal impairments for patients with chronic stroke who lack residual movements. Methods . A 67-year-old hemiplegic patient with no active finger extension was trained with a brain—computer interface (BCI) combined with a specific daily life—oriented physiotherapy. The BCI used electrical brain activity (EEG) and magnetic brain activity (MEG) to drive an orthosis and a robot affixed to the patient’s affected upper extremity, which enabled him to move the paralyzed arm and hand driven by voluntary modulation of μ-rhythm activity. In addition, the patient practiced goal-directed physiotherapy training. Over 1 year, he completed 3 training blocks. Arm motor function, gait capacities (using Fugl-Meyer Assessment, Wolf Motor Function Test, Modified Ashworth Scale, 10-m walk speed, and goal attainment score), and brain reorganization (functional MRI, MEG) were repeatedly assessed. Results. The ability of hand and arm movements as well as speed and safety of gait improved significantly (mean 46.6%). Improvement of motor function was associated with increased μ-oscillations in the ipsilesional motor cortex. Conclusion. This proof-of-principle study suggests that the combination of BCI training with goal-directed, active physical therapy may improve the motor abilities of chronic stroke patients despite apparent initial paralysis.

Localizing Sources of Brain Activity Relevant to Motor Imagery Brain-Computer Interface Performance, Using Individual Head Geometry

2012 ◽  
pp. 369-378
Author(s):  
Alexander A. Frolov ◽  
Dušan Húsek ◽  
Pavel D. Bobrov ◽  
Alexey Korshakov ◽  
Lyudmila Chernikova ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface
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