6 papers
Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Jun-Yu Pan, Yansen Wang, Enze Zhang +3
Leveraging the universal representations of pre-trained LLMs and MLLMs offers a promising path toward brain foundation models. However, visually-evoked EEG datasets remain scarce,…
MindCine: Multimodal EEG-to-Video Reconstruction with Large-Scale Pretrained Models
Tian-Yi Zhou, Xuan-Hao Liu, Bao-Liang Lu +1
Reconstructing human dynamic visual perception from electroencephalography (EEG) signals is of great research significance since EEG's non-invasiveness and high temporal resolution…
MindCross: Fast New Subject Adaptation with Limited Data for Cross-subject Video Reconstruction from Brain Signals
Xuan-Hao Liu, Yan-Kai Liu, Tianyi Zhou +2
Reconstructing video from brain signals is an important brain decoding task. Existing brain decoding frameworks are primarily built on a subject-dependent paradigm, which requires…
mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup
Xuan-Hao Liu, Bao-Liang Lu, Wei-Long Zheng
The cross-subject electroencephalography (EEG) classification exhibits great challenges due to the diversity of cognitive processes and physiological structures between different s…
NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals
Wei-Bang Jiang, Yansen Wang, Bao-Liang Lu +1
Recent advancements for large-scale pre-training with neural signals such as electroencephalogram (EEG) have shown promising results, significantly boosting the development of brai…
Professor X: Manipulating EEG BCI with Invisible and Robust Backdoor Attack
Xuan-Hao Liu, Xinhao Song, Dexuan He +2
While electroencephalogram (EEG) based brain-computer interface (BCI) has been widely used for medical diagnosis, health care, and device control, the safety of EEG BCI has long be…