1 citations · 4 across the 10 of their papers we have counts for
13 papers
Learning Dynamic Neural Evidence Representations for Time-Adaptive Brain-Computer Interfaces
Beining Cao, Ziyi Zhao, Xiaowei Jiang +5
Brain-computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed-window decoding that may result in redundant observation or unreliabl…
Online Inference of Human Intention as a Latent Control State from Single-Trial EEG
Xiaowei Jiang, Daniel Leong, Yu-Cheng Chang +2
Human intention can be modeled as a latent internal state that modulates how sensory information is evaluated and translated into action in human-machine systems. However, most exi…
iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration
Xiaowei Jiang, Daniel Leong, Beining Cao +5
Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reas…
Interpretable Fuzzy Modeling Reveals Population-Level Representation Differences in P300 Brain Computer Interfaces Across Neurodivergent and Neurotypical Cohorts
Xiaowei Jiang, Sudong Shang, Adrian Wilkinson +4
P300-based brain-computer interfaces (BCIs) are widely used for communication, but population heterogeneity may alter the neural patterns available for decoding. Prior work has mai…
SASLO: A Scene-Aware Spatial Layout Optimization System for AR-SSVEP
Beining Cao, Xiaowei Jiang, Charlie Li-Ting Tsai +3
Steady-state visual evoked potential (SSVEP) is widely used in brain-computer interfaces (BCIs) due to its reliability. With the integration of augmented reality (AR), AR-SSVEP ena…
BrainStack: Neuro-MoE with Functionally Guided Expert Routing for EEG-Based Language Decoding
Ziyi Zhao, Jinzhao Zhou, Xiaowei Jiang +6
Decoding linguistic information from electroencephalography (EEG) remains challenging due to the brain's distributed and nonlinear organization. We present BrainStack, a functional…
Pretraining Large Brain Language Model for Active BCI: Silent Speech
Jinzhao Zhou, Zehong Cao, Yiqun Duan +9
This paper explores silent speech decoding in active brain-computer interface (BCI) systems, which offer more natural and flexible communication than traditional BCI applications.…
Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces
Xiaowei Jiang, Yanan Chen, Nikhil Ranjan Pal +4
Fuzzy logic provides a robust framework for enhancing explainability, particularly in domains requiring the interpretation of complex and ambiguous signals, such as brain-computer…
EMD-Fuzzy: An Empirical Mode Decomposition Based Fuzzy Model for Cross-Stimulus Transfer Learning of SSVEP
Beining Cao, Xiaowei Jiang, Daniel Leong +4
The Brain-Computer Interface (BCI) enables direct brain-to-device communication, with the Steady-State Visual Evoked Potential (SSVEP) paradigm favored for its stability and high a…
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification
Xiaowei Jiang, Wenhao Ma, Yiqun Duan +2
In the realm of digital forensics and document authentication, writer identification plays a crucial role in determining the authors of documents based on handwriting styles. The p…
Neural Spelling: A Spell-Based BCI System for Language Neural Decoding
Xiaowei Jiang, Charles Zhou, Yiqun Duan +3
Brain-computer interfaces (BCIs) present a promising avenue by translating neural activity directly into text, eliminating the need for physical actions. However, existing non-inva…
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System
Xiaowei Jiang, Beining Cao, Liang Ou +3
The rapid evolution of Brain-Computer Interfaces (BCIs) has significantly influenced the domain of human-computer interaction, with Steady-State Visual Evoked Potentials (SSVEP) em…
E2H: A Two-Stage Non-Invasive Neural Signal Driven Humanoid Robotic Whole-Body Control Framework
Yiqun Duan, Qiang Zhang, Jinzhao Zhou +9
Recent advancements in humanoid robotics, including the integration of hierarchical reinforcement learning-based control and the utilization of LLM planning, have significantly enh…