Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning
Electroencephalogram (EEG) signals play an important role in brain-computer interface
(BCI) applications. Recent studies have utilized transfer learning to assist the learning task …
(BCI) applications. Recent studies have utilized transfer learning to assist the learning task …
Online Seizure Prediction via Fine-Tuning and Test-Time Adaptation
Privacy protection has become increasingly crucial in the field of epilepsy prediction. Some
latest studies introduced the source-free domain adaptation (SFDA), which only utilizes a …
latest studies introduced the source-free domain adaptation (SFDA), which only utilizes a …
Towards Domain-free Transformer for Generalized EEG Pre-training
Electroencephalography (EEG) signals are the brain signals acquired using the non-
invasive approach. Owing to the high portability and practicality, EEG signals have found …
invasive approach. Owing to the high portability and practicality, EEG signals have found …
Motor imagery classification for asynchronous EEG-based brain-computer interfaces
Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of
external devices through the imagined movements of various body parts. Unlike previous …
external devices through the imagined movements of various body parts. Unlike previous …
Channel reflection: Knowledge-driven data augmentation for EEG-based brain–computer interfaces
A brain–computer interface (BCI) enables direct communication between the human brain
and external devices. Electroencephalography (EEG) based BCIs are currently the most …
and external devices. Electroencephalography (EEG) based BCIs are currently the most …
Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs
Machine learning has achieved great success in electroencephalogram (EEG) based brain-
computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding …
computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding …
Tailoring Deep Learning for Real-Time Brain-Computer Interfaces: From Offline Models to Calibration-Free Online Decoding
The success of deep learning (DL) in offline brain-computer interfaces (BCIs) has not yet
translated into efficient online applications. This is due to two limiting factors: the need for …
translated into efficient online applications. This is due to two limiting factors: the need for …
[PDF][PDF] ATTA: Adaptive Test-Time Adaptation for Multi-Modal Sleep Stage Classification
Z Jia, X Yang, C Zhou, H Deng, T Jiang, B Center - ijcai.org
Sleep stage classification is crucial for sleep quality assessment and disease diagnosis.
Although some recent studies have made great strides in sleep stage classification …
Although some recent studies have made great strides in sleep stage classification …
A Fully Test-Time Adaptation Method for Motor Imagery
Y Peng, L Chen, S Guo, Y Guo, Y Li - Li and Guo, Shijie and Guo … - papers.ssrn.com
Transfer learning offers a promising solution to the annotation and calibration challenges
associated with motor imagery (MI) electroencephalogram (EEG) decoding during neural …
associated with motor imagery (MI) electroencephalogram (EEG) decoding during neural …
Channel Reflection: Knowledge-Driven Data Augmentation for Eeg-Based Bcis
Z Wang, S Li, J Luo, J Liu, D Wu - Available at SSRN 4682921 - papers.ssrn.com
A brain-computer interface (BCI) enables direct communication between the human brain
and external devices. Electroencephalography (EEG) based BCIs are currently the most …
and external devices. Electroencephalography (EEG) based BCIs are currently the most …