EEG motor imagery classification with sparse spectrotemporal decomposition and deep learning
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks raises a big
challenge in the design and development of brain-computer interfaces (BCIs). In view of the …
challenge in the design and development of brain-computer interfaces (BCIs). In view of the …
Mutual information-driven subject-invariant and class-relevant deep representation learning in BCI
In recent years, deep learning-based feature representation methods have shown a
promising impact on electroencephalography (EEG)-based brain–computer interface (BCI) …
promising impact on electroencephalography (EEG)-based brain–computer interface (BCI) …
Deep CNN model based on serial-parallel structure optimization for four-class motor imagery EEG classification
Motor imagery electroencephalogram (MI-EEG) is one of the most important brain-computer
interface (BCI) signal. It is vital to analyze the MI-EEG for the manipulation of external BCI …
interface (BCI) signal. It is vital to analyze the MI-EEG for the manipulation of external BCI …
Multi-view multi-scale optimization of feature representation for EEG classification improvement
Effectively extracting common space pattern (CSP) features from motor imagery (MI) EEG
signals is often highly dependent on the filter band selection. At the same time, optimizing …
signals is often highly dependent on the filter band selection. At the same time, optimizing …
Driver drowsiness detection: An approach based on intelligent brain–computer interfaces
Estimating reaction times (RTs) and drowsiness states from brain signals is a notable step in
creating passive brain–computer interfaces (BCIs). Prior to the deep learning era, estimating …
creating passive brain–computer interfaces (BCIs). Prior to the deep learning era, estimating …
Cross-channel specific-mutual feature transfer learning for motor imagery EEG signals decoding
D Li, J Wang, J Xu, X Fang, Y Ji - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
In recent years, with the rapid development of deep learning, various deep learning
frameworks have been widely used in brain-computer interface (BCI) research for decoding …
frameworks have been widely used in brain-computer interface (BCI) research for decoding …
A Human–Machine Joint Learning Framework to Boost Endogenous BCI Training
Brain–computer interfaces (BCIs) provide a direct pathway from the brain to external devices
and have demonstrated great potential for assistive and rehabilitation technologies …
and have demonstrated great potential for assistive and rehabilitation technologies …
Meditation and cognitive enhancement: A machine learning based classification using eeg
Meditation methods, which have their origins in ancient traditions are gaining popularity as a
result of their potential mental and physical health advantages. EEG neural correlates …
result of their potential mental and physical health advantages. EEG neural correlates …
E‐CNNet: Time‐reassigned Multisynchrosqueezing transform‐based deep learning framework for MI‐BCI task classification
M Kaur, R Upadhyay, V Kumar - International Journal of …, 2023 - Wiley Online Library
The classification of electroencephalograms‐based motor imagery signals poses a
significant issue in the design and development of brain‐computer interfaces. Neural …
significant issue in the design and development of brain‐computer interfaces. Neural …
Statistical evaluation of factors influencing inter-session and inter-subject variability in eeg-based brain computer interface
A cognitive alteration in the form of diverse mental states has a significant impact on the
performance of electroencephalography (EEG) based brain computer interface (BCI). Such …
performance of electroencephalography (EEG) based brain computer interface (BCI). Such …