2 citations · 6 across the 9 of their papers we have counts for
13 papers
SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts
Shanglin Li, Shiwen Chu, Okan Koç +5
Electroencephalography (EEG) based brain-computer interfaces enable direct brain-to-device communication for applications such as rehabilitation and communication. However, their p…
Explaining Hyperbolic Neural Networks via Geometry-Aware Relevance Propagation
Ping Xiong, Shanglin Li, Yi Ding +2
Hyperbolic neural networks introduce geometric operations that require explicit treatment in relevance propagation. Equivalent geometric realizations can produce different feature…
HEEGNet: Hyperbolic Embeddings for EEG
Shanglin Li, Shiwen Chu, Okan Koç +4
Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. Howeve…
EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training
Yuting Tang, Weibang Jiang, Shanglin Li +5
Large-scale EEG foundation models have shown strong generalization across a range of downstream tasks, but their training remains resource-intensive due to the volume and variable…
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…
ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models
Chenyu Liu, Yuqiu Deng, Tianyu Liu +4
Electroencephalography (EEG), with its broad range of applications, necessitates models that can generalize effectively across various tasks and datasets. Large EEG Models (LEMs) a…
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…
BrainPro: Process-conditioned Pretraining with Topographic Retrieval for Large EEG Models
Yi Ding, Muyun Jiang, Weibang Jiang +6
EEG foundation models are pretrained on heterogeneous corpora, but two challenges remain underexplored: (1) experimental protocols provide mental-process cues that are rarely used…
Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery
Xinliang Zhou, Chenyu Liu, Zhisheng Chen +4
Brain foundation models (BFMs) have emerged as a transformative paradigm in computational neuroscience, offering a revolutionary framework for processing diverse neural signals acr…
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…
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…