Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
[HTML][HTML] An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification
Abstract Electroencephalogram (EEG)-based Brain–Computer Interfaces (BCIs) build a
communication path between human brain and external devices. Among EEG-based BCI …
communication path between human brain and external devices. Among EEG-based BCI …
MTLFuseNet: a novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning
R Li, C Ren, Y Ge, Q Zhao, Y Yang, Y Shi… - Knowledge-Based …, 2023 - Elsevier
How to extract discriminative latent feature representations from electroencephalography
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …
A shallow mirror transformer for subject-independent motor imagery BCI
Objective: Motor imagery BCI plays an increasingly important role in motor disorders
rehabilitation. However, the position and duration of the discriminative segment in an EEG …
rehabilitation. However, the position and duration of the discriminative segment in an EEG …
A temporal dependency learning CNN with attention mechanism for MI-EEG decoding
Deep learning methods have been widely explored in motor imagery (MI)-based brain
computer interface (BCI) systems to decode electroencephalography (EEG) signals …
computer interface (BCI) systems to decode electroencephalography (EEG) signals …
M2FN: An end-to-end multi-task and multi-sensor fusion network for intelligent fault diagnosis
Intelligent fault diagnosis based on multi-sensor fusion has gained considerable attention in
various modern industrial applications. However, it is still challenging to extract …
various modern industrial applications. However, it is still challenging to extract …
EEG-channel-temporal-spectral-attention correlation for motor imagery EEG classification
WY Hsu, YW Cheng - IEEE Transactions on Neural Systems …, 2023 - ieeexplore.ieee.org
In brain-computer interface (BCI) work, how correctly identifying various features and their
corresponding actions from complex Electroencephalography (EEG) signals is a …
corresponding actions from complex Electroencephalography (EEG) signals is a …
EEG-Deformer: A dense convolutional transformer for brain-computer interfaces
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is
challenging yet essential for decoding brain activities using brain-computer interfaces …
challenging yet essential for decoding brain activities using brain-computer interfaces …
FBMSNet: A filter-bank multi-scale convolutional neural network for EEG-based motor imagery decoding
K Liu, M Yang, Z Yu, G Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Object: Motor imagery (MI) is a mental process widely utilized as the experimental paradigm
for brain-computer interfaces (BCIs) across a broad range of basic science and clinical …
for brain-computer interfaces (BCIs) across a broad range of basic science and clinical …
Interpretable and robust ai in eeg systems: A survey
The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …
substantially advanced human-computer interaction (HCI) technologies in the AI era …