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20242026
most citedInterpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces

1 citations · 2 across the 8 of their papers we have counts for

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6 papers · 1 filter

eess.SP2026

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…

cs.CR2026

When Context Bites: Detecting RAG Poisoning via Document-Level Attention Collapse

Yingtao Ren, Ziyi Zhao, Yiwei Fu +3

Retrieval-augmented generation (RAG) is indispensable for enhancing large language models. However, RAGs are increasingly susceptible to poisoning attacks, in which adversarial doc…

cs.HC2026

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…

cs.LG2026

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…

cs.CV2026

DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis

Cheng-You Lu, Yi-Shan Hung, Wei-Ling Chi +6

Advances in radiance fields have enabled photorealistic novel view synthesis. In several domains, large-scale real-world datasets have been developed to support comprehensive bench…

cs.CV2026

Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control

Zhuoli Zhuang, Yu-Cheng Chang, Yu-Kai Wang +2

Recent advancements in computer vision have accelerated the development of autonomous driving. Despite these advancements, training machines to drive in a way that aligns with huma…