AIMC Topic: Brain-Computer Interfaces

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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 Task Performance and Analysis Support Vector Machine 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 ...
Databases as Topic Machine Learning Brain-Computer Interfaces Humans Algorithms Models, Statistical Electroencephalography
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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...
Humans Machine Learning Algorithms Brain-Computer Interfaces Motor Activity Imagination Discriminant Analysis Electroencephalography Brain Datasets as Topic Time Factors Linear Models Bayes Theorem
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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 Machine Learning Online Systems 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 ...
Middle Aged Hand Strength Robotics Hand Brain Female Adult Male Quadriplegia Brain-Computer Interfaces Movement Upper Extremity Neurological Rehabilitation Humans
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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 Humans Machine Learning User-Computer Interface Support Vector Machine Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces
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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...
Brain-Computer Interfaces Robotics Photic Stimulation Electroencephalography Event-Related Potentials, P300 Brain Models, Biological Spatial Navigation Computer Simulation Biomechanical Phenomena Fuzzy Logic Humans
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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 Support Vector Machine Humans Brain-Computer Interfaces Models, Neurological Algorithms Movement Models, Statistical Databases, Factual Imagination Electroencephalography
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Training and testing ERP-BCIs under different mental workload conditions.

Journal of neural engineering Dec 10, 2015
OBJECTIVE: As one of the most popular and extensively studied paradigms of brain-computer interfaces (BCIs), event-related potential-based BCIs (ERP-BCIs) are usually built and tested in ideal laboratory settings in most existing studies, with subjec...
Humans Machine Learning Female Reproducibility of Results Young Adult Adult Sensitivity and Specificity Male Brain-Computer Interfaces Task Performance and Analysis Event-Related Potentials, P300 Communication Devices for People with Disabilities Cognition Electroencephalography Workload Pattern Recognition, Automated Word Processing
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Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Nov 20, 2015
The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibra...
Middle Aged Adolescent Humans Electroencephalography Calibration Reproducibility of Results Machine Learning Signal Processing, Computer-Assisted Brain-Computer Interfaces Algorithms Young Adult Photic Stimulation Female Adult Male
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