A review on transfer learning in EEG signal analysis
Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
A review of epileptic seizure detection using machine learning classifiers
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain
signals produced by brain neurons. Neurons are connected to each other in a complex way …
signals produced by brain neurons. Neurons are connected to each other in a complex way …
Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states
Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …
review the physical principles of BCIs, and underlying novel approaches for registration …
[HTML][HTML] An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals
Background: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental
disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and …
disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and …
A review of EEG signal features and their application in driver drowsiness detection systems
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …
is often approached using neurophysiological signals as the basis for building a reliable …
Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals
Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …
Chaos as an intermittently forced linear system
Understanding the interplay of order and disorder in chaos is a central challenge in modern
quantitative science. Approximate linear representations of nonlinear dynamics have long …
quantitative science. Approximate linear representations of nonlinear dynamics have long …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
Microstates in resting-state EEG: current status and future directions
Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the
brain with high temporal resolution. Several analytical approaches to extract information …
brain with high temporal resolution. Several analytical approaches to extract information …
Evolutionary inspired approach for mental stress detection using EEG signal
Stress is a pensive issue in our competitive world and it has a huge impact on physical and
mental health. Severe health issues may arise due to long exposure of stress. Hence, its …
mental health. Severe health issues may arise due to long exposure of stress. Hence, its …