2 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
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…