From the 1 of 7 linked papers with an AI index.
7 papers
Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision
Zhiyuan Ma, Zeyuan Li, Zhiyi Lu +7
The paper introduces BridgeMIL, a two-stage method that first learns EEG instance representations without using inherited labels and then applies subject-level supervision via a mu…
Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining
Junfeng Xia, Wenhao Ye, Xuanye Pan +3
Current fMRI foundation models primarily rely on a limited range of brain states and mismatched pretraining tasks, restricting their ability to learn generalized representations ac…
OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models
Ziling Lu, Zongsheng Li, Xinke Shen +11
Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions. EEG foundation models are eme…
A geometry aware framework enhances noninvasive mapping of whole human brain dynamics
Song Wang, Kexin Lou, Chen Wei +8
Non-invasive electrophysiology lacks methods that accurately reconstruct whole-brain spatiotemporal dynamics while incorporating individual cortical geometry, leaving current elect…
DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding
Zhiyuan Ma, Zeyuan Li, Zihao Qiu +6
In real-world applications of noninvasive electroencephalography (EEG), specialized decoders often show limited generalizability across diverse tasks under subject-independent sett…
Multi-dataset Joint Pre-training of Emotional EEG Enables Generalizable Affective Computing
Qingzhu Zhang, Jiani Zhong, Zongsheng Li +2
Task-specific pre-training is essential when task representations diverge from generic pre-training features. Existing task-general pre-training EEG models struggle with complex ta…