AIMC Topic: Brain-Computer Interfaces
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Information Theoretic Feature Transformation Learning for Brain Interfaces.
IEEE transactions on bio-medical engineering
Mar 28, 2019
OBJECTIVE: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential s...
Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition.
IEEE transactions on cybernetics
Mar 27, 2019
Electroencephalogram (EEG) has been widely used in emotion recognition due to its high temporal resolution and reliability. Since the individual differences of EEG are large, the emotion recognition models could not be shared across persons, and we n...
Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition.
Neural computation
Mar 18, 2019
Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an a...
On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-Based Bio-Signal Decoding in BCI Speller Applications.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 13, 2019
Brain-computer interfaces (BCI) harnessing steady state visual evoked potentials (SSVEPs) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with al...
Learning joint space-time-frequency features for EEG decoding on small labeled data.
Neural networks : the official journal of the International Neural Network Society
Mar 11, 2019
Brain-computer interfaces (BCIs), which control external equipment using cerebral activity, have received considerable attention recently. Translating brain activities measured by electroencephalography (EEG) into correct control commands is a critic...
Cyborg groups enhance face recognition in crowded environments.
PloS one
Mar 6, 2019
Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interf...
Portable brain-computer interface based on novel convolutional neural network.
Computers in biology and medicine
Mar 4, 2019
Electroencephalography (EEG) is a powerful, noninvasive tool that provides a high temporal resolution to directly reflect brain activities. Conventional electrodes require skin preparation and the use of conductive gels, while subjects must wear unco...
Deep learning for electroencephalogram (EEG) classification tasks: a review.
Journal of neural engineering
Feb 26, 2019
OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools ...
Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.
Computers in biology and medicine
Feb 19, 2019
In motor imagery (MI) based brain-computer interface (BCI) signal analysis, mu and beta rhythms of electroencephalograms (EEGs) are widely investigated due to their high temporal resolution and capability to define the different movement-related ment...
Vision-aided brain-machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury.
Biomedical engineering online
Feb 11, 2019
BACKGROUND: While spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate metho...
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