AIMC Topic: Brain-Computer Interfaces
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The Connection Between the Nervous System and Machines: Commentary.
Journal of medical Internet research
Nov 6, 2019
Decades of technological developments have populated the field of brain-machine interfaces and neuroprosthetics with several replacement strategies, neural modulation treatments, and rehabilitation techniques to improve the quality of life for patien...
Spiking Neural Networks applied to the classification of motor tasks in EEG signals.
Neural networks : the official journal of the International Neural Network Society
Oct 16, 2019
Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern recognition, we report on the development and evaluation of brain signal classifiers based on SNNs. The work shows the capabilities of this type of Spiking Neurons in ...
Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces.
Neural networks : the official journal of the International Neural Network Society
Sep 20, 2019
OBJECTIVE: This paper argues that Brain-Inspired Spiking Neural Network (BI-SNN) architectures can learn and reveal deep in time-space functional and structural patterns from spatio-temporal data. These patterns can be represented as deep knowledge, ...
A zero-shot learning approach to the development of brain-computer interfaces for image retrieval.
PloS one
Sep 16, 2019
Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which ...
Visual Evoked Response Modulation Occurs in a Complementary Manner Under Dynamic Circuit Framework.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 12, 2019
The steady-state visual-evoked potential (SSVEP) induced by the periodic visual stimulus plays an important role in vision research. An increasing number of studies use the SSVEP to manipulate intrinsic oscillation and further regulate test performan...
Recognition of words from brain-generated signals of speech-impaired people: Application of autoencoders as a neural Turing machine controller in deep neural networks.
Neural networks : the official journal of the International Neural Network Society
Sep 9, 2019
There is an essential requirement to support people with speech and communication disabilities. A brain-computer interface using electroencephalography (EEG) is applied to satisfy this requirement. A number of research studies to recognize brain sign...
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.
Computational intelligence and neuroscience
Sep 9, 2019
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. H...
World's fastest brain-computer interface: Combining EEG2Code with deep learning.
PloS one
Sep 6, 2019
We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can either be used in a passive Brain-Computer Interface (BCI) to predict properties of a visual stimulus...
Multi optimized SVM classifiers for motor imagery left and right hand movement identification.
Australasian physical & engineering sciences in medicine
Aug 30, 2019
EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG s...
A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 29, 2019
One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various...
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