16 citations · 24 across the 20 of their papers we have counts for
12 papers · 1 filter
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
Peiliang Gong, Han Zhang, Zhen Jiang +5
Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing…
Introducing Multimodal Paradigm for Learning Sleep Staging PSG via General-Purpose Model
Jianheng Zhou, Chenyu Liu, Jinan Zhou +5
Sleep staging is essential for diagnosing sleep disorders and assessing neurological health. Existing automatic methods typically extract features from complex polysomnography (PSG…
Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning
Zhisheng Chen, Yingwei Zhang, Qizhen Lan +7
Current foundation models for electroencephalography (EEG) rely on architectures adapted from computer vision or natural language processing, typically treating neural signals as p…
SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment
Siyuan Zhao, Chenyu Liu, Yi Ding +1
In practical sleep stage classification, a key challenge is the variability of EEG data across different subjects and environments. Differences in physiology, age, health status, a…
BiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging
Xinliang Zhou, Yuzhe Han, Zhisheng Chen +4
In this paper, we address the challenges in automatic sleep stage classification, particularly the high computational cost, inadequate modeling of bidirectional temporal dependenci…
A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective
Chenyu Liu, Xinliang Zhou, Yihao Wu +5
Compared to other modalities, electroencephalogram (EEG) based emotion recognition can intuitively respond to emotional patterns in the human brain and, therefore, has become one o…
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
Yi Ding, Yong Li, Hao Sun +5
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs).…
Graph Neural Networks in EEG-based Emotion Recognition: A Survey
Chenyu Liu, Yuqiu Deng, Yihao Wu +10
Compared to other modalities, EEG-based emotion recognition can intuitively respond to the emotional patterns in the human brain and, therefore, has become one of the most concerni…
EEG-based Sleep Staging with Hybrid Attention
Xinliang Zhou, Chenyu Liu, Jiaping Xiao +1
Sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. However, capturing both the spatial and temporal relationships within electroencephalogram (EE…
EENED: End-to-End Neural Epilepsy Detection based on Convolutional Transformer
Chenyu Liu, Xinliang Zhou, Yang Liu
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in EEG signal processing. Transformer models can capture the global dependencies…
An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance
Xinliang Zhou, Dan Lin, Ziyu Jia +4
Drowsy driving has a crucial influence on driving safety, creating an urgent demand for driver drowsiness detection. Electroencephalogram (EEG) signal can accurately reflect the me…
Interpretable and Robust AI in EEG Systems: A Survey
Xinliang Zhou, Chenyu Liu, Jinan Zhou +5
The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has substantially advanced human-computer interaction (HCI) technologies in the AI era. Differen…