Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review
O Maslova, Y Komarova, N Shusharina… - Frontiers in Human …, 2023 - frontiersin.org
The defeat of the central motor neuron leads to the motor disorders. Patients lose the ability
to control voluntary muscles, for example, of the upper limbs, which introduces a …
to control voluntary muscles, for example, of the upper limbs, which introduces a …
Survey on the research direction of EEG-based signal processing
C Sun, C Mou - Frontiers in Neuroscience, 2023 - frontiersin.org
Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI)
systems due to its portability and simplicity. In this paper, we provide a comprehensive …
systems due to its portability and simplicity. In this paper, we provide a comprehensive …
EEGSym: Overcoming inter-subject variability in motor imagery based BCIs with deep learning
S Pérez-Velasco… - … on Neural Systems …, 2022 - ieeexplore.ieee.org
In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI)
based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to …
based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to …
Optimal channels and features selection based ADHD detection from EEG signal using statistical and machine learning techniques
Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and
neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based …
neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based …
[HTML][HTML] Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface
R Dhiman - Measurement: Sensors, 2023 - Elsevier
Abstract Decryption of Motor Imagery (MI) activity from an Electroencephalogram (EEG) data
is a significant part of the Brain-Computer Interface (BCI) technology that allows motor …
is a significant part of the Brain-Computer Interface (BCI) technology that allows motor …
Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals
Objective. Brain–computer interfaces (BCIs) enable a direct communication pathway
between the human brain and external devices, without relying on the traditional peripheral …
between the human brain and external devices, without relying on the traditional peripheral …
Effects of motor imagery tasks on brain functional networks based on EEG Mu/Beta rhythm
H Yu, S Ba, Y Guo, L Guo, G Xu - Brain Sciences, 2022 - mdpi.com
Motor imagery (MI) refers to the mental rehearsal of movement in the absence of overt motor
action, which can activate or inhibit cortical excitability. EEG mu/beta oscillations recorded …
action, which can activate or inhibit cortical excitability. EEG mu/beta oscillations recorded …
Relevance-based channel selection in motor imagery brain–computer interface
Objective. Channel selection in the electroencephalogram (EEG)-based brain–computer
interface (BCI) has been extensively studied for over two decades, with the goal being to …
interface (BCI) has been extensively studied for over two decades, with the goal being to …
[HTML][HTML] Dementia classification using a graph neural network on imaging of effective brain connectivity
Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms
of neurodegenerative diseases. The literature suggests that effective brain connectivity …
of neurodegenerative diseases. The literature suggests that effective brain connectivity …
EEGNet-MSD: A sparse convolutional neural network for efficient EEG-based intent decoding
R Fu, Z Wang, S Wang, X Xu, J Chen… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Electroencephalography (EEG) is a noninvasive technique that can be used in brain
machine interface (BMI) systems to measure and record brain electrical activity. Deep …
machine interface (BMI) systems to measure and record brain electrical activity. Deep …