A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …
on brain signals aims to discover the underlying neurological or physical status of the …
[PDF][PDF] A survey on deep learning based brain computer interface: Recent advances and new frontiers
Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …
by decoding individuals' brain signals into commands recognizable by computer devices …
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
Objective. Brain–computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally chosen from a …
using neural activity as the control signal. This neural signal is generally chosen from a …
Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials
Objective. Steady-state visual evoked potentials (SSVEPs) are neural oscillations from the
parietal and occipital regions of the brain that are evoked from flickering visual stimuli …
parietal and occipital regions of the brain that are evoked from flickering visual stimuli …
IENet: a robust convolutional neural network for EEG based brain-computer interfaces
Y Du, J Liu - Journal of neural engineering, 2022 - iopscience.iop.org
Objective. Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) develop
into novel application areas with more complex scenarios, which put forward higher …
into novel application areas with more complex scenarios, which put forward higher …
Enhancing cross-subject transfer performance for SSVEP identification using small data-based transferability evaluation
Cross-subject steady-state visual evoked potential-based brain-computer interfaces (SSVEP-
BCIs) have attracted increasing attention in recent years due to their potential to reduce …
BCIs) have attracted increasing attention in recent years due to their potential to reduce …
Temporal-spatial cross attention network for recognizing imagined characters
Previous research has primarily employed deep learning models such as Convolutional
Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined …
Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined …
[HTML][HTML] Classification of targets and distractors in an audiovisual attention task based on electroencephalography
Within the broader context of improving interactions between artificial intelligence and
humans, the question has arisen regarding whether auditory and rhythmic support could …
humans, the question has arisen regarding whether auditory and rhythmic support could …
Decoding P300 variability using convolutional neural networks
Deep convolutional neural networks (CNN) have previously been shown to be useful tools
for signal decoding and analysis in a variety of complex domains, such as image processing …
for signal decoding and analysis in a variety of complex domains, such as image processing …
[HTML][HTML] Saccade size predicts onset time of object processing during visual search of an open world virtual environment
Objective To date the vast majority of research in the visual neurosciences have been forced
to adopt a highly constrained perspective of the vision system in which stimuli are processed …
to adopt a highly constrained perspective of the vision system in which stimuli are processed …