Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces
AM Roy - Biomedical Signal Processing and Control, 2022 - Elsevier
Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
LSTM-based EEG classification in motor imagery tasks
P Wang, A Jiang, X Liu, J Shang… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Classification of motor imagery electroencephalograph signals is a fundamental problem in
brain–computer interface (BCI) systems. We propose in this paper a classification framework …
brain–computer interface (BCI) systems. We propose in this paper a classification framework …
Riemannian flow matching on general geometries
We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training
continuous normalizing flows on manifolds. Existing methods for generative modeling on …
continuous normalizing flows on manifolds. Existing methods for generative modeling on …
A deep transfer convolutional neural network framework for EEG signal classification
Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
Transfer learning: A Riemannian geometry framework with applications to brain–computer interfaces
Objective: This paper tackles the problem of transfer learning in the context of
electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In …
electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In …
Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network
H Li, M Ding, R Zhang, C Xiu - Biomedical signal processing and control, 2022 - Elsevier
Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer
interaction. Traditional neural networks often use serial structure to extract spatial features …
interaction. Traditional neural networks often use serial structure to extract spatial features …
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 …
Multiclass brain–computer interface classification by Riemannian geometry
A Barachant, S Bonnet, M Congedo… - IEEE Transactions on …, 2011 - ieeexplore.ieee.org
This paper presents a new classification framework for brain-computer interface (BCI) based
on motor imagery. This framework involves the concept of Riemannian geometry in the …
on motor imagery. This framework involves the concept of Riemannian geometry in the …