73 citations · 275 across the 13 of their papers we have counts for
13 papers
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…
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…
Focused State Recognition Using EEG with Eye Movement-Assisted Annotation
Tian-Hua Li, Tian-Fang Ma, Dan Peng +2
With the rapid advancement in machine learning, the recognition and analysis of brain activity based on EEG and eye movement signals have attained a high level of sophistication. U…
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals
Yu-Ting Lan, Kan Ren, Yansen Wang +4
Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery. Thanks to the recent advances in both…
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition
Xun Wu, Wei-Long Zheng, Bao-Liang Lu
Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity…
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
Wei Liu, Jie-Lin Qiu, Wei-Long Zheng +1
Multimodal signals are more powerful than unimodal data for emotion recognition since they can represent emotions more comprehensively. In this paper, we introduce deep canonical c…
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data
Changde Du, Changying Du, Hao Wang +4
There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to m…
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition
Changde Du, Changying Du, Jinpeng Li +3
In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly…
A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG
Wei-Long Zheng, Bao-Liang Lu
Objective. Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estima…
Multimodal Emotion Recognition Using Multimodal Deep Learning
Wei Liu, Wei-Long Zheng, Bao-Liang Lu
To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to con…
Identifying Stable Patterns over Time for Emotion Recognition from EEG
Wei-Long Zheng, Jia-Yi Zhu, Bao-Liang Lu
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of acti…