AIMC Topic: Brain-Computer Interfaces

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Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 21, 2016
Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nerv...
Computer Simulation Animals Male Robotics Brain-Computer Interfaces Task Performance and Analysis Joints Exoskeleton Device Electroencephalography Biofeedback, Psychology Artificial Limbs Models, Biological Man-Machine Systems Macaca mulatta Feedback, Physiological
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Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 7, 2016
In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG sign...
Imagination Pattern Recognition, Automated Humans Sensitivity and Specificity Brain-Computer Interfaces Machine Learning Brain Mapping Movement Algorithms Electroencephalography Evoked Potentials, Motor Reproducibility of Results Linear Models
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Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

Australasian physical & engineering sciences in medicine Jul 4, 2016
Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine contro...
Machine Learning Female Adult Male Humans Neural Networks, Computer Signal Processing, Computer-Assisted Pattern Recognition, Automated Brain-Computer Interfaces Support Vector Machine Task Performance and Analysis Principal Component Analysis Least-Squares Analysis Electroencephalography
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Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

Medical & biological engineering & computing Apr 21, 2016
Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model ...
Electroencephalography Databases as Topic Brain-Computer Interfaces Humans Machine Learning Algorithms Models, Statistical
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Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification.

International journal of neural systems Apr 11, 2016
Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification a...
Linear Models Brain-Computer Interfaces Motor Activity Imagination Bayes Theorem Discriminant Analysis Humans Electroencephalography Machine Learning Datasets as Topic Algorithms Brain Time Factors
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Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Mar 18, 2016
Electroencephalography (EEG) headsets are the most commonly used sensing devices for brain-computer interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limi...
Electroencephalography Equipment Design Electrodes Pattern Recognition, Automated Brain-Computer Interfaces Equipment Failure Analysis Online Systems Machine Learning Algorithms Reproducibility of Results Sensitivity and Specificity Humans
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Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

Journal of neuroengineering and rehabilitation Mar 18, 2016
BACKGROUND: Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of ...
Humans Hand Brain-Computer Interfaces Movement Upper Extremity Neurological Rehabilitation Quadriplegia Male Middle Aged Hand Strength Robotics Brain Female Adult
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A Prototype SSVEP Based Real Time BCI Gaming System.

Computational intelligence and neuroscience Mar 9, 2016
Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradig...
Evoked Potentials Video Games Support Vector Machine Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Machine Learning User-Computer Interface
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A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface.

Computational intelligence and neuroscience Dec 24, 2015
We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a varie...
Models, Biological Brain-Computer Interfaces Photic Stimulation Event-Related Potentials, P300 Fuzzy Logic Robotics Electroencephalography Spatial Navigation Humans Brain Computer Simulation Biomechanical Phenomena
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Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

Computational intelligence and neuroscience Dec 22, 2015
Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve ...
Probability Brain-Computer Interfaces Movement Imagination Support Vector Machine Models, Neurological Models, Statistical Electroencephalography Humans Algorithms Databases, Factual
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