[HTML][HTML] A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation
This literature review explores the pivotal role of brain–computer interface (BCI) technology,
coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for …
coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for …
EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification
Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
A Survey of EEG and Machine Learning based methods for Neural Rehabilitation
One approach to therapy and training for the restoration of damaged muscles and motor
systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in …
systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in …
Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces
Background: Analysis and classification of extensive medical data (eg
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
electroencephalography (EEG) signals) is a significant challenge to develop effective brain …
Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework
Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
Hybrid binary grey wolf with Harris hawks optimizer for feature selection
Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in
local optima areas may still be a concern. Several significant GWO factors can be explored …
local optima areas may still be a concern. Several significant GWO factors can be explored …
Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features
Schizophrenia is a mental disorder that causes adverse effects on the mental capacity of a
person, emotional inclinations, and quality of personal and social life. The official statistics …
person, emotional inclinations, and quality of personal and social life. The official statistics …
Motor imagery BCI classification based on novel two‐dimensional modelling in empirical wavelet transform
Brain complexity and non‐stationary nature of electroencephalography (EEG) signal make
considerable challenges for the accurate identification of different motor‐imagery (MI) tasks …
considerable challenges for the accurate identification of different motor‐imagery (MI) tasks …
Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features
Depression is a mental disorder that continues to make life difficult or impossible for a
depressed person and, if left untreated, can lead to dangerous activities such as self-harm …
depressed person and, if left untreated, can lead to dangerous activities such as self-harm …
A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems
Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic
components or modes from electroencephalogram (EEG) signals for the development of …
components or modes from electroencephalogram (EEG) signals for the development of …