Data augmentation for deep-learning-based electroencephalography
Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …
considerable performance gains for deep learning (DL)—increased accuracy and stability …
Brain-computer interface: Advancement and challenges
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain–computer interface
AM Roy - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
Abstract Objective. Deep learning (DL)-based brain–computer interface (BCI) in motor
imagery (MI) has emerged as a powerful method for establishing direct communication …
imagery (MI) has emerged as a powerful method for establishing direct communication …
A sliding window common spatial pattern for enhancing motor imagery classification in EEG-BCI
Accurate binary classification of electroencephalography (EEG) signals is a challenging task
for the development of motor imagery (MI) brain–computer interface (BCI) systems. In this …
for the development of motor imagery (MI) brain–computer interface (BCI) systems. In this …
HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification
G Dai, J Zhou, J Huang, N Wang - Journal of neural engineering, 2020 - iopscience.iop.org
Objective. Electroencephalography (EEG) motor imagery classification has been widely
used in healthcare applications such as mobile assistive robots and post-stroke …
used in healthcare applications such as mobile assistive robots and post-stroke …
Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network
In recent years, deep learning has emerged as a powerful tool for developing Brain–
Computer Interface (BCI) systems. However, for deep learning models trained entirely on the …
Computer Interface (BCI) systems. However, for deep learning models trained entirely on the …
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 …
Deep representation-based domain adaptation for nonstationary EEG classification
In the context of motor imagery, electroencephalography (EEG) data vary from subject to
subject such that the performance of a classifier trained on data of multiple subjects from a …
subject such that the performance of a classifier trained on data of multiple subjects from a …
A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry
A brain-computer interface (BCI) facilitates a medium to translate the human motion
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
Improved domain adaptation network based on Wasserstein distance for motor imagery EEG classification
Q She, T Chen, F Fang, J Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in
brain-computer interface (BCI) technology have facilitated the detection of MI from …
brain-computer interface (BCI) technology have facilitated the detection of MI from …