Author

Chenyu Liu

8 papers indexed here

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Review Sep 2026

Brain-to-Language Decoding: Paradigms, Signals, Methods, Evaluation, Practical Use and Beyond

A prospective five-level trajectory from commands and language to meaning, scenarios and bidirectional cognitive exchange is outlined, then a prospective five-level trajectory from commands and language to meaning, scenarios and bidirectional cognitive exchange is outlined.

Yi-Qian Yang, Yi-Qun Duan, Chen-Yu Liu et al. · 1 citation

Review Sep 2026

Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond

Brain-to-language decoding translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs. It offers a route to restoring communication after speech loss and a means of studying how the brain represents language. Advances in neural recording and repr...

Yi-Qian Yang, Yi-Qun Duan, Chen-Yu Liu et al. · 1 citation

#machine learning Preprint Oct 2026

DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models

Disentangled EFM Fine-Tuning (DEFT) is proposed, a plug-and-play adapter for EFM fine-tuning that improves matched fine-tuning baselines and encourages branch disentanglement of pretrained tokens into task-oriented content and subject-consistent style.

Yu-Qiu Deng, Muyun Jiang, Zong-Peng Zhang et al. · 0 citations

Conference Open access Sep 2026

FedGLoRA: Grassmann-Manifold Federated Learning via Dual LoRA for Large EEG Models

FedGLoRA freezes the backbone and communicates only LoRA adapters via a dual-branch design, and aggregates global updates on a Grassmann subspace with a Grassmann-proximal constraint, and uses bidirectional distillation to mitigate imbalance.

Qianyu Chen, Yi-Hao Zhong, Run-Xuan Tang et al. · Proceedings of the Thirty-Fi... · 0 citations

#machine learning Preprint Oct 2026

SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts

Electroencephalography (EEG) based brain-computer interfaces enable direct brain-to-device communication for applications such as rehabilitation and communication. However, their practical utility is often limited as the non-stationary nature of the EEG data introduces distribution shifts across domains (e.g., sessions...

Shang-Lin Li, Shi-Wen Chu, Okan Koç et al. · 0 citations

#machine learning Preprint Oct 2026

ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models

EEG foundation models (EFMs) capture reusable knowledge from large-scale EEG data, while many EEG recordings also include companion physiological signals that provide complementary information beyond the EEG-only interface. The challenge is to preserve this pretrained knowledge while extending the EFM to heterogeneous...

Yu-Bo Wang, Jing-Ying Ma, Xin-Liang Zhou et al. · 0 citations

#natural language process... Review Sep 2026

Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond

This survey connects Articulated, Inner and Perceived tasks to the neural populations they engage, the representations available to decoders and the outputs those representations can support, and outlines a prospective five-level trajectory from commands and language to meaning, scenarios and bidirectional cognitive ex...

Yi-Qian Yang, Yi-Qun Duan, Chen-Yu Liu et al. · 1 citation

Aug 2026

Multi-Scale BiMamba with Test-Time Adaptation for Motor Imagery Classification

Hybrid brain-computer interfaces combining electroencephalography and functional near-infrared spectroscopy utilize neurovascular coupling to improve decoding. Current fusion strategies rely on shallow aggregation or complex attention, which struggle to capture complex temporal dynamics and complex dependencies in long...

Ziyang Bao, Mingseng Guo, Botao Jin et al. · ACM Transactions on Autonomo... · 0 citations