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 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 …
Alcoholic EEG signals recognition based on phase space dynamic and geometrical features
Alcoholism is a severe disorder that leads to brain problems and associated cognitive,
emotional and behavioral impairments. This disorder is typically diagnosed by a …
emotional and behavioral impairments. This disorder is typically diagnosed by a …
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 …
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 …
[HTML][HTML] Eeg-based alzheimer's disease recognition using robust-pca and lstm recurrent neural network
M Alessandrini, G Biagetti, P Crippa, L Falaschetti… - Sensors, 2022 - mdpi.com
The use of electroencephalography (EEG) has recently grown as a means to diagnose
neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can …
neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can …
Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain
A widespread brain disorder of present days is depression which influences 264 million of
the world's population. Depression may cause diverse undesirable consequences, including …
the world's population. Depression may cause diverse undesirable consequences, including …
Toward the development of versatile brain–computer interfaces
Recent advances in artificial intelligence demand an automated framework for the
development of versatile brain–computer interface (BCI) systems. In this article, we …
development of versatile brain–computer interface (BCI) systems. In this article, we …
A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment
K Kyriaki, D Koukopoulos, CA Fidas - IEEE Access, 2024 - ieeexplore.ieee.org
Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive
Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly …
Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly …