Applications of higher order statistics in electroencephalography signal processing: A comprehensive survey
SA Khoshnevis, R Sankar - IEEE Reviews in biomedical …, 2019 - ieeexplore.ieee.org
Electroencephalography (EEG) is a noninvasive electrophysiological monitoring technique
that records the electrical activities of the brain from the scalp using electrodes. EEG is not …
that records the electrical activities of the brain from the scalp using electrodes. EEG is not …
Empirical mode decomposition and its extensions applied to EEG analysis: a review
Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–
frequency analysis, yielding components from which local amplitude, phase, and frequency …
frequency analysis, yielding components from which local amplitude, phase, and frequency …
Second-order synchroextracting transform with application to fault diagnosis
Synchrosqueezing transform (SST) is a currently proposed novel postprocessing time-
frequency (TF) analysis tool. It has been widely shown that SST is able to improve TF …
frequency (TF) analysis tool. It has been widely shown that SST is able to improve TF …
Time-frequency distribution map-based convolutional neural network (CNN) model for underwater pipeline leakage detection using acoustic signals
Detection technology of underwater pipeline leakage plays an important role in the subsea
production system. In this paper, a new method based on the acoustic leak signal collected …
production system. In this paper, a new method based on the acoustic leak signal collected …
A robust adaptive overcurrent relay coordination scheme for wind-farm-integrated power systems based on forecasting the wind dynamics for smart energy systems
Conventional protection schemes in the distribution system are liable to suffer from high
penetration of renewable energy source-based distributed generation (RES-DG). The …
penetration of renewable energy source-based distributed generation (RES-DG). The …
Changes in EEG permutation entropy in the evening and in the transition from wake to sleep
F Hou, L Zhang, B Qin, G Gaggioni, X Liu… - Sleep, 2021 - academic.oup.com
Quantifying the complexity of the EEG signal during prolonged wakefulness and during
sleep is gaining interest as an additional mean to characterize the mechanisms associated …
sleep is gaining interest as an additional mean to characterize the mechanisms associated …
Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms
Abstract Background Hepatitis C virus (HCV) causes chronic hepatitis C in 2-3% of world
population and remains one of the health threatening human viruses, worldwide. In the …
population and remains one of the health threatening human viruses, worldwide. In the …
Evaluation of EEG features in decoding individual finger movements from one hand
With the advancements in modern signal processing techniques, the field of brain‐computer
interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding …
interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding …
Understanding the underlying mechanism of HA-subtyping in the level of physic-chemical characteristics of protein
The evolution of the influenza A virus to increase its host range is a major concern
worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza …
worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza …
Sinusoidal signal assisted multivariate empirical mode decomposition for brain–computer interfaces
A brain-computer interface (BCI) is a communication approach that permits cerebral activity
to control computers or external devices. Brain electrical activity recorded with …
to control computers or external devices. Brain electrical activity recorded with …