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 …
A survey of deep neural network architectures and their applications
Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep
learning techniques have drawn ever-increasing research interests because of their …
learning techniques have drawn ever-increasing research interests because of their …
Deep learning in the biomedical applications: Recent and future status
Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
Speech synthesis from ECoG using densely connected 3D convolutional neural networks
Objective. Direct synthesis of speech from neural signals could provide a fast and natural
way of communication to people with neurological diseases. Invasively-measured brain …
way of communication to people with neurological diseases. Invasively-measured brain …
Deep learning in fNIRS: a review
Significance: Optical neuroimaging has become a well-established clinical and research
tool to monitor cortical activations in the human brain. It is notable that outcomes of …
tool to monitor cortical activations in the human brain. It is notable that outcomes of …
Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification
Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous
system to a device. BCI was historically performed using electroencephalography (EEG). In …
system to a device. BCI was historically performed using electroencephalography (EEG). In …
A systematic review on hybrid EEG/fNIRS in brain-computer interface
As a relatively new field of neurology and computer science, brain computer interface (BCI)
has many established and burgeoning applications across scientific disciplines. Many …
has many established and burgeoning applications across scientific disciplines. Many …
A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near …
Shutdown maintenance, ie, turning off a facility for a short period for renewal or replacement
operations is a highly stressful task. With the limited time and complex operation procedures …
operations is a highly stressful task. With the limited time and complex operation procedures …
Enhanced accuracy for multiclass mental workload detection using long short-term memory for brain–computer interface
Cognitive workload is one of the widely invoked human factors in the areas of human–
machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and …
machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and …