Non-invasive Brain-Computer Interfaces: State of the Art and Trends
Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …
Dynamic subcluster-aware network for few-shot skin disease classification
This article addresses the problem of few-shot skin disease classification by introducing a
novel approach called the subcluster-aware network (SCAN) that enhances accuracy in …
novel approach called the subcluster-aware network (SCAN) that enhances accuracy in …
Subject-independent meta-learning framework towards optimal training of eeg-based classifiers
HW Ng, C Guan - Neural Networks, 2024 - Elsevier
Advances in deep learning have shown great promise towards the application of performing
high-accuracy Electroencephalography (EEG) signal classification in a variety of tasks …
high-accuracy Electroencephalography (EEG) signal classification in a variety of tasks …
A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning
Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer
interaction by enabling direct links between the brain and computer systems. Recent studies …
interaction by enabling direct links between the brain and computer systems. Recent studies …
A Few-Shot Transfer Learning Approach for Motion Intention Decoding from Electroencephalographic Signals.
In this study, a few-shot transfer learning approach was introduced to decode movement
intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with …
intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with …
Explainable cross-task adaptive transfer learning for motor imagery EEG classification
M Miao, Z Yang, H Zeng, W Zhang… - Journal of Neural …, 2023 - iopscience.iop.org
Objective. In the field of motor imagery (MI) electroencephalography (EEG)-based brain-
computer interfaces, deep transfer learning (TL) has proven to be an effective tool for solving …
computer interfaces, deep transfer learning (TL) has proven to be an effective tool for solving …
Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features
In industrial manufacturing sites, defect detection is crucial to improve reliability and lower
inspection costs. Though prior anomaly detectors have shown success, they rely on large …
inspection costs. Though prior anomaly detectors have shown success, they rely on large …
Reducing vulnerable internal feature correlations to enhance efficient topological structure parsing
Z Lin, Z Zheng, J Jia, W Gao - Expert Systems with Applications, 2024 - Elsevier
Most cropping-and-segmenting pattern parsers typically establish a single metric/scheme to
reason diverse inner correlations, resulting in over-general and redundant representations …
reason diverse inner correlations, resulting in over-general and redundant representations …
Learn to Supervise: Deep Reinforcement Learning-Based Prototype Refinement for Few-Shot Motor Fault Diagnosis
P Xia, Y Huang, C Liu, J Liu - IEEE Transactions on Neural …, 2024 - ieeexplore.ieee.org
Motor fault diagnosis is a fundamental aspect of ensuring the reliability of industrial
equipment. However, industrial scenarios exhibit an inherent data scarcity problem, which …
equipment. However, industrial scenarios exhibit an inherent data scarcity problem, which …
Cross-dataset motor imagery decoding—A transfer learning assisted graph convolutional network approach
The proliferation of portable electroencephalogram (EEG) recording devices has made it
practically feasible to develop the motor imagery (MI) based brain–computer interfaces …
practically feasible to develop the motor imagery (MI) based brain–computer interfaces …