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Brain-Computer Interfaces

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Signal identification system for developing rehabilitative device using deep learning algorithms.

Artificial intelligence in medicine
Paralyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and Muscular dystrophy affect the muscle movements in the body. The affected persons were unable to mig...

The Last Mile: Where Artificial Intelligence Meets Reality.

Journal of medical Internet research
Although much effort is focused on improving the technical performance of artificial intelligence, there are compelling reasons to focus more on the implementation of this technology class to solve real-world applications. In this "last mile" of impl...

The Connection Between the Nervous System and Machines: Commentary.

Journal of medical Internet research
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
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
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
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
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
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
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
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...