299 citations · 301 across the 7 of their papers we have counts for
7 papers
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…
Capturing Aperiodic Temporal Dynamics of EEG Signals through Stochastic Fluctuation Modeling
Yuhao Sun, Zhiyuan Ma, Xinke Shen +3
Electrophysiological brain signals, such as electroencephalography (EEG), exhibit both periodic and aperiodic components, with the latter often modeled as 1/f noise and considered…
Dynamic-Attention-based EEG State Transition Modeling for Emotion Recognition
Xinke Shen, Runmin Gan, Kaixuan Wang +5
Electroencephalogram (EEG)-based emotion decoding can objectively quantify people's emotional state and has broad application prospects in human-computer interaction and early dete…
A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces
Yuntian Cui, Xinke Shen, Dan Zhang +1
ERP-based EEG detection is gaining increasing attention in the field of brain-computer interfaces. However, due to the complexity of ERP signal components, their low signal-to-nois…
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience
Xinke Shen, Lingyi Tao, Xuyang Chen +3
Neural representations induced by naturalistic stimuli offer insights into how humans respond to stimuli in daily life. Understanding neural mechanisms underlying naturalistic stim…
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Peizhen Yang, Xinke Shen, Zongsheng Li +3
Identifying causal relationships among distinct brain areas, known as effective connectivity, holds key insights into the brain's information processing and cognitive functions. El…
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition
Xinke Shen, Xianggen Liu, Xin Hu +2
EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still p…