Deep learning for motor imagery EEG-based classification: A review
Objectives The availability of large and varied Electroencephalogram (EEG) datasets,
rapidly advances and inventions in deep learning techniques, and highly powerful and …
rapidly advances and inventions in deep learning techniques, and highly powerful and …
[HTML][HTML] Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
Sparse Bayesian learning for end-to-end EEG decoding
Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-
computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG …
computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG …
Complex networks and deep learning for EEG signal analysis
Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …
representation of the human's physiological and pathological states. Up to now, much work …
[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 …
CWT based transfer learning for motor imagery classification for brain computer interfaces
Background The processing of brain signals for Motor imagery (MI) classification to have
better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional …
better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
MI-EEGNET: A novel convolutional neural network for motor imagery classification
Background Brain–computer interfaces (BCI) permits humans to interact with machines by
decoding brainwaves to command for a variety of purposes. Convolutional neural networks …
decoding brainwaves to command for a variety of purposes. Convolutional neural networks …
Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination
D Borra, S Fantozzi, E Magosso - Neural Networks, 2020 - Elsevier
Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding:
these techniques, by automatically learning relevant features for class discrimination …
these techniques, by automatically learning relevant features for class discrimination …
Feature extraction method based on filter banks and Riemannian tangent space in motor-imagery BCI
Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-
BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most …
BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most …