1 citations · 1 across the 2 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…