7 papers
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…
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…
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…