From the 1 of 7 linked papers with an AI index.
7 papers
EEG-JEPA: Structured Latent Prediction for EEG Foundation Models
Jinhao Li, Zhiyuan Ma, Xueqiao Han +8
Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconst…
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…
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…
LI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding
Chenghao Yue, Zhiyuan Ma, Zhongye Xia +4
Electroencephalography (EEG) provides a non-invasive window into brain activity, offering high temporal resolution crucial for understanding and interacting with neural processes t…
Training-Driven Representational Geometry Modularization Predicts Brain Alignment in Language Models
Yixuan Liu, Zhiyuan Ma, Likai Tang +5
How large language models (LLMs) align with the neural representation and computation of human language is a central question in cognitive science. Using representational geometry…
Signal-Adaptive Trust Regions for Gradient-Free Optimization of Recurrent Spiking Neural Networks
Jinhao Li, Yuhao Sun, Zhiyuan Ma +5
Recurrent spiking neural networks (RSNNs) are a promising substrate for energy-efficient control policies, but training them for high-dimensional, long-horizon reinforcement learni…