Deep learning for motor imagery EEG-based classification: A review
Objectives The availability of large and varied Electroencephalogram (EEG) datasets,
rapidly advances and inventions in deep learning techniques, and highly powerful and …
rapidly advances and inventions in deep learning techniques, and highly powerful and …
[HTML][HTML] Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques
The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …
numerous applications in biomedical fields, including sleep and the brain–computer …
Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN
Vibration signals have been increasingly utilized in various engineering fields for analysis
and monitoring purposes, including structural health monitoring, fault diagnosis and damage …
and monitoring purposes, including structural health monitoring, fault diagnosis and damage …
Optimization of cnn using modified honey badger algorithm for sleep apnea detection
Sleep Apnea (SA) is the most prevalent breathing sleep problem, and if left untreated, it can
lead to catastrophic neurological and cardiovascular illnesses. Conventionally …
lead to catastrophic neurological and cardiovascular illnesses. Conventionally …
[HTML][HTML] Lemurs optimizer: A new metaheuristic algorithm for global optimization
The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper.
This algorithm's primary inspirations are based on two pillars of lemur behavior: leap up and …
This algorithm's primary inspirations are based on two pillars of lemur behavior: leap up and …
[HTML][HTML] Smart home battery for the multi-objective power scheduling problem in a smart home using grey wolf optimizer
The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling
operations of smart home appliances under a set of restrictions and a dynamic pricing …
operations of smart home appliances under a set of restrictions and a dynamic pricing …
S-EEGNet: Electroencephalogram signal classification based on a separable convolution neural network with bilinear interpolation
W Huang, Y Xue, L Hu, H Liuli - IEEE Access, 2020 - ieeexplore.ieee.org
As one of the most important research fields in the brain–computer interface (BCI) field,
electroencephalogram (EEG) classification has a wide range of application values …
electroencephalogram (EEG) classification has a wide range of application values …
[HTML][HTML] An improved feature extraction algorithms of EEG signals based on motor imagery brain-computer interface
X Geng, D Li, H Chen, P Yu, H Yan, M Yue - Alexandria Engineering …, 2022 - Elsevier
The electroencephalogram (EEG) signals based on the Brian-computer Interface (BCI)
equipment is weak, non-linear, non-stationary and time-varying, so an effective feature …
equipment is weak, non-linear, non-stationary and time-varying, so an effective feature …
[HTML][HTML] IC-U-Net: a U-Net-based denoising autoencoder using mixtures of independent components for automatic EEG artifact removal
Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative
to develop a practical and reliable artifact removal method to prevent the misinterpretation of …
to develop a practical and reliable artifact removal method to prevent the misinterpretation of …
Comparison of signal processing methods considering their optimal parameters using synthetic signals in a heat exchanger network simulation
É Thibault, FL Désilets, B Poulin, M Chioua… - Computers & Chemical …, 2023 - Elsevier
Plant sensor data contain errors that can hamper process analysis and decision-making.
Those dataset are not used to their full potential due to the complexity of their processing …
Those dataset are not used to their full potential due to the complexity of their processing …