AIMC Topic: Brain-Computer Interfaces

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Classifier transfer with data selection strategies for online support vector machine classification with class imbalance.

Journal of neural engineering Feb 13, 2017
OBJECTIVE: Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the sup...
Humans Algorithms Brain Computer Simulation Electroencephalography Data Mining Pattern Recognition, Automated Brain-Computer Interfaces Online Systems Reproducibility of Results Sensitivity and Specificity Support Vector Machine Models, Neurological
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EXiO-A Brain-Controlled Lower Limb Exoskeleton for Rhesus Macaques.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 26, 2017
Recent advances in the field of brain-machine interfaces (BMIs) have demonstrated enormous potential to shape the future of rehabilitation and prosthetic devices. Here, a lower-limb exoskeleton controlled by the intracortical activity of an awake beh...
Task Performance and Analysis Equipment Failure Analysis Exoskeleton Device Macaca mulatta Equipment Design Gait Artificial Limbs Brain-Computer Interfaces Sensitivity and Specificity Animals Robotics Female Reproducibility of Results
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Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.

Journal of neural engineering Jan 10, 2017
OBJECTIVE: Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, ...
Sensitivity and Specificity Male Unsupervised Machine Learning Data Interpretation, Statistical Electroencephalography Humans Female Reproducibility of Results Adult Statistics as Topic Brain Waves Visual Perception Visual Cortex Signal Processing, Computer-Assisted Pattern Recognition, Automated Brain-Computer Interfaces Evoked Potentials, Visual
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Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review.

Journal of neural engineering Jan 9, 2017
Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently ...
Humans Brain-Computer Interfaces Algorithms Muscle Contraction Brain Evoked Potentials Support Vector Machine Man-Machine Systems Muscle, Skeletal Electroencephalography Robotics Electromyography Pattern Recognition, Automated
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Local and Remote Cooperation With Virtual and Robotic Agents: A P300 BCI Study in Healthy and People Living With Spinal Cord Injury.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Dec 23, 2016
The development of technological applications that allow people to control and embody external devices within social interaction settings represents a major goal for current and future brain-computer interface (BCI) systems. Prior research has sugges...
User-Computer Interface Robotics Brain-Computer Interfaces Task Performance and Analysis Event-Related Potentials, P300 Man-Machine Systems Imagination Spinal Cord Injuries Movement Young Adult Adult Sensitivity and Specificity Male Humans Female Reproducibility of Results
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Evolvix BEST Names for semantic reproducibility across code2brain interfaces.

Annals of the New York Academy of Sciences Dec 5, 2016
Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward...
Brain-Computer Interfaces Terminology as Topic Cloud Computing Software Design Internet Data Mining Programming Languages Biological Ontologies Humans Software Reproducibility of Results Computational Biology
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A novel deep learning approach for classification of EEG motor imagery signals.

Journal of neural engineering Nov 30, 2016
OBJECTIVE: Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number ...
Sensitivity and Specificity Humans Movement Electroencephalography Machine Learning Evoked Potentials, Motor Pattern Recognition, Automated Algorithms Imagination Brain-Computer Interfaces Reproducibility of Results Sensorimotor Cortex
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Automated selection of brain regions for real-time fMRI brain-computer interfaces.

Journal of neural engineering Nov 30, 2016
OBJECTIVE: Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site exp...
Unsupervised Machine Learning Brain Mapping Principal Component Analysis Humans Brain-Computer Interfaces Female Sensitivity and Specificity Word Processing Reproducibility of Results Task Performance and Analysis Male Adult Nerve Net Computer Systems Visual Perception Magnetic Resonance Imaging Visual Cortex
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Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces.

PLoS computational biology Nov 23, 2016
Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligi...
Speech Acoustics Communication Devices for People with Disabilities Speech Production Measurement Sound Spectrography Humans Brain-Computer Interfaces Neural Networks, Computer Speech Intelligibility Computer Systems Phonetics Biofeedback, Psychology
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The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

Journal of neuroscience methods Nov 11, 2016
BACKGROUND: Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong back...
Machine Learning Brain Female Young Adult Signal Processing, Computer-Assisted Brain-Computer Interfaces Evoked Potentials, Visual Male Electroencephalography Humans Motion Perception
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