Identification of lower-limb motor tasks via brain–computer interfaces: a topical overview
Recent engineering and neuroscience applications have led to the development of brain–
computer interface (BCI) systems that improve the quality of life of people with motor …
computer interface (BCI) systems that improve the quality of life of people with motor …
[HTML][HTML] Decoding movement kinematics from EEG using an interpretable convolutional neural network
Continuous decoding of hand kinematics has been recently explored for the intuitive control
of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural …
of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural …
A deep neural network for ssvep-based brain-computer interfaces
OB Guney, M Oblokulov… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Objective: Target identification in brain-computer interface (BCI) spellers refers to the
electroencephalogram (EEG) classification for predicting the target character that the subject …
electroencephalogram (EEG) classification for predicting the target character that the subject …
TRCA-net: using TRCA filters to boost the SSVEP classification with convolutional neural network
Objective. The steady-state visual evoked potential (SSVEP)-based brain–computer
interface has received extensive attention in research due to its simple system, less training …
interface has received extensive attention in research due to its simple system, less training …
Education 4.0: teaching the basics of KNN, LDA and simple perceptron algorithms for binary classification problems
One of the main focuses of Education 4.0 is to provide students with knowledge on
disruptive technologies, such as Machine Learning (ML), as well as the skills to implement …
disruptive technologies, such as Machine Learning (ML), as well as the skills to implement …
Automatic detection of schizophrenia based on spatial–temporal feature mapping and LeViT with EEG signals
B Li, J Wang, Z Guo, Y Li - Expert Systems with Applications, 2023 - Elsevier
Electroencephalography (EEG) signals, which record brain activity, are known to be very
useful in diagnosing brain-related conditions. However, manual examination of these EEG …
useful in diagnosing brain-related conditions. However, manual examination of these EEG …
Multivariate fast iterative filtering based automated system for grasp motor imagery identification using EEG signals
One of the most crucial use of hands in daily life is grasping. Sometimes people with
neuromuscular disorders become incapable of moving their hands. This article proposes a …
neuromuscular disorders become incapable of moving their hands. This article proposes a …
Eye State Identification Utilizing EEG Signals: A Combined Method Using Self‐Organizing Map and Deep Belief Network
Measuring brain activity through Electroencephalogram (EEG) analysis for eye state
prediction has attracted attention from machine learning researchers. There have been …
prediction has attracted attention from machine learning researchers. There have been …
CNN-based approaches for cross-subject classification in motor imagery: From the state-of-the-art to DynamicNet
The accurate detection of motor imagery (MI) from electroencephalography (EEG) is a
fundamental, as well as challenging, task to provide reliable control of robotic devices to …
fundamental, as well as challenging, task to provide reliable control of robotic devices to …
Comparison of attention-based deep learning models for eeg classification
Objective: To evaluate the impact on Electroencephalography (EEG) classification of
different kinds of attention mechanisms in Deep Learning (DL) models. Methods: We …
different kinds of attention mechanisms in Deep Learning (DL) models. Methods: We …