EEG artifact removal—state-of-the-art and guidelines
This paper presents an extensive review on the artifact removal algorithms used to remove
the main sources of interference encountered in the electroencephalogram (EEG) …
the main sources of interference encountered in the electroencephalogram (EEG) …
[HTML][HTML] From brain to movement: Wearables-based motion intention prediction across the human nervous system
Fueled by the recent proliferation of energy-efficient and energy-autonomous or self-
powered nanotechnology-based wearable smart systems, human motion intention …
powered nanotechnology-based wearable smart systems, human motion intention …
New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms
Abstract Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering
(IF) are largely implemented for representing a signal as superposition of simpler well …
(IF) are largely implemented for representing a signal as superposition of simpler well …
A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry
A brain-computer interface (BCI) facilitates a medium to translate the human motion
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
[HTML][HTML] Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing
Rolling bearings are widely used in rotary machinery systems. The measured vibration
signal of any part linked to rolling bearings contains fault information when failure occurs …
signal of any part linked to rolling bearings contains fault information when failure occurs …
Seizure detection from EEG signals using multivariate empirical mode decomposition
We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG
signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a …
signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a …
Wearable in-ear PPG: Detailed respiratory variations enable classification of COPD
An ability to extract detailed spirometry-like breathing waveforms from wearable sensors
promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has …
promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has …
An automatic subject specific intrinsic mode function selection for enhancing two-class EEG-based motor imagery-brain computer interface
The electroencephalogram (EEG) signals tend to have poor time-frequency localization
when analysis techniques involve a fixed set of basis functions such as in short-time Fourier …
when analysis techniques involve a fixed set of basis functions such as in short-time Fourier …
Tangent space features-based transfer learning classification model for two-class motor imagery brain–computer interface
The performance of a brain–computer interface (BCI) will generally improve by increasing
the volume of training data on which it is trained. However, a classifier's generalization …
the volume of training data on which it is trained. However, a classifier's generalization …
[HTML][HTML] Smart helmet: Wearable multichannel ECG and EEG
Modern wearable technologies have enabled continuous recording of vital signs, however,
for activities such as cycling, motor-racing, or military engagement, a helmet with embedded …
for activities such as cycling, motor-racing, or military engagement, a helmet with embedded …