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 …
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 …
Correlation-filter-based channel and feature selection framework for hybrid EEG-fNIRS BCI applications
The proposed study is based on a feature and channel selection strategy that uses
correlation filters for brain–computer interface (BCI) applications using …
correlation filters for brain–computer interface (BCI) applications using …
Cross-modal multiscale multi-instance learning for long-term ECG classification
At present, deep learning models have been widely used in electrocardiogram (ECG)
classification. However, when processing ECG signals over a long period of time, it is …
classification. However, when processing ECG signals over a long period of time, it is …
Transfer learning based deep network for signal restoration and rhythm analysis during cardiopulmonary resuscitation using only the ECG waveform
Y Gong, L Wei, S Yan, F Zuo, H Zhang, Y Li - Information Sciences, 2023 - Elsevier
Minimizing the interruption of cardiopulmonary resuscitation (CPR) is an important
technique to improve the survival of out-of-hospital cardiac arrest (OHCA) patients. Recent …
technique to improve the survival of out-of-hospital cardiac arrest (OHCA) patients. Recent …
Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have
potentially complementary characteristics that reflect the electrical and hemodynamic …
potentially complementary characteristics that reflect the electrical and hemodynamic …
A resource-efficient ECG diagnosis model for mobile health devices
Mobile health devices with automatic electrocardiogram diagnosis models facilitate long-
term cardiac monitoring and enhance the sensitivity of detecting paroxysmal cardiovascular …
term cardiac monitoring and enhance the sensitivity of detecting paroxysmal cardiovascular …
[HTML][HTML] Orthogonal semi-supervised regression with adaptive label dragging for cross-session EEG emotion recognition
T Sha, Y Peng - Journal of King Saud University-Computer and …, 2023 - Elsevier
Owning to its merits of great temporal resolution, portability and low cost,
electroencephalogram (EEG) signals have received increasing attention in emotion …
electroencephalogram (EEG) signals have received increasing attention in emotion …
A hybrid deep leaning model for prediction and parametric sensitivity analysis of noise annoyance
Noise annoyance is recognized as an expression of physiological and psychological strain
in acoustical environment. The studies on prediction of noise annoyance and parametric …
in acoustical environment. The studies on prediction of noise annoyance and parametric …
A heuristic approach to the hyperparameters in training spiking neural networks using spike-timing-dependent plasticity
The third type of neural network called spiking is developed due to a more accurate
representation of neuronal activity in living organisms. Spiking neural networks have many …
representation of neuronal activity in living organisms. Spiking neural networks have many …