Deep learning for electroencephalogram (EEG) classification tasks: a review
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
Deep learning-based electroencephalography analysis: a systematic review
Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …
of training, as well as advanced signal processing and feature extraction methodologies to …
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 study of deep learning approach for the classification of Electroencephalogram (EEG) brain signals
Electroencephalography (EEG) signals denote the electric activities of the brain. They are
measured in microvolt (μV). There are various methods for the collection of raw data from …
measured in microvolt (μV). There are various methods for the collection of raw data from …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
Wearable system based on ultra-thin Parylene C tattoo electrodes for EEG recording
In an increasingly interconnected world, where electronic devices permeate every aspect of
our lives, wearable systems aimed at monitoring physiological signals are rapidly taking …
our lives, wearable systems aimed at monitoring physiological signals are rapidly taking …
Deep learning methods for EEG neural classification
Classification of patterns of brain activity in neuroengineering research is an important tool
for understanding the brain, developing neurodiagnostics, and designing closed-loop neural …
for understanding the brain, developing neurodiagnostics, and designing closed-loop neural …
Adaptive swarm decomposition guided by spectral characteristic information scanner and its application for bearing fault diagnosis
Swarm decomposition (SWD) is an emerging signal decomposition method and has been
applied in the fault diagnosis of rotating machinery. However, the performance of SWD is …
applied in the fault diagnosis of rotating machinery. However, the performance of SWD is …
Brain-computer interfaces, open-source, and democratizing the future of augmented consciousness
Accessibility, adaptability, and transparency of Brain-Computer Interface (BCI) tools and the
data they collect will likely impact how we collectively navigate a new digital age. This …
data they collect will likely impact how we collectively navigate a new digital age. This …