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 …
Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices
through the utilization of brain waves. It is worth noting that the application of BCI is not …
through the utilization of brain waves. It is worth noting that the application of BCI is not …
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 …
A comparative analysis of signal processing and classification methods for different applications based on EEG signals
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2020 - Elsevier
Electroencephalogram (EEG) measures the neuronal activities in the form of electric
currents that are generated due to the synchronized activity by a group of specialized …
currents that are generated due to the synchronized activity by a group of specialized …
Automated detection of schizophrenia using nonlinear signal processing methods
Examination of the brain's condition with the Electroencephalogram (EEG) can be helpful to
predict abnormality and cerebral activities. The purpose of this study was to develop an …
predict abnormality and cerebral activities. The purpose of this study was to develop an …
A deep learning model for automated sleep stages classification using PSG signals
Sleep disorder is a symptom of many neurological diseases that may significantly affect the
quality of daily life. Traditional methods are time-consuming and involve the manual scoring …
quality of daily life. Traditional methods are time-consuming and involve the manual scoring …
Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals
Emotion is interpreted as a psycho-physiological process, and it is associated with
personality, behavior, motivation, and character of a person. The objective of affective …
personality, behavior, motivation, and character of a person. The objective of affective …
Parkinson's disease: Cause factors, measurable indicators, and early diagnosis
S Bhat, UR Acharya, Y Hagiwara, N Dadmehr… - Computers in biology …, 2018 - Elsevier
Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system
caused due to the loss of dopaminergic neurons. It is classified under movement disorder as …
caused due to the loss of dopaminergic neurons. It is classified under movement disorder as …
Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram
U Erdenebayar, YJ Kim, JU Park, EY Joo… - Computer methods and …, 2019 - Elsevier
Abstract Background and Objective This study demonstrates deep learning approaches with
an aim to find the optimal method to automatically detect sleep apnea (SA) events from an …
an aim to find the optimal method to automatically detect sleep apnea (SA) events from an …