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Kaizhong Zheng

5 accepted papers

2026

Spontaneous Yet Predictable: Shapelet-Driven, Channel-Aware Intention Decoding from Multi-Region ECoG

AAAI 2026technical

Proactive intention decoding remains a critical yet underexplored challenge in brain–machine interfaces (BMIs), especially under naturalistic, self-initiated behavior. Existing systems rely on reactive decoding of motor cortex signals, resulting in substantial latency. To address this, we leverage t

Cited by 0SourcePDFScholar
2026

Structured Multi-modal Graph Disentanglement for Psychiatric Diagnosis

ICML 2026poster

Multi-modal neuroimaging diagnosis must integrate cross-modal agreement with modality-specific complementarity, yet in real multi-site cohorts these signals are frequently entangled with site- and cohort-dependent correlations, yielding shortcut-driven predictions, fragile transfer, and limited inte…

Cited by 0SourceScholar
2025

Local-Global Coupling Spiking Graph Transformer for Brain Disorders Diagnosis from Two Perspectives

NeurIPS 2025poster

Brain disorders have been consistently associated with abnormalities in specific brain regions or neural circuits. Identifying key brain regional activities and functional connectivity patterns is essential for discovering more precise neurobiological biomarkers. However, previous studies have prima…

Cited by 0SourceScholar
2024

Predicting RTMS Treatment Effects Using Open-Loop Control and Neural Manifold

ICASSP 2024accepted

Repetitive transcranial magnetic stimulation (rTMS) is a common non-invasive treatment for medication-resistant major depressive disorder (MDD). It utilizes continuous and adjustable magnetic stimulation to modulate neural circuits implicated in the pathogenesis of depression. Nevertheless, construc…

Cited by 0SourceScholar
2023

Towards a More Stable and General Subgraph Information Bottleneck

ICASSP 2023accepted

Graph Neural Networks (GNNs) have been widely applied to graph-structured data. However, the lack of interpretability impedes its practical deployment especially in high-risk areas such as medical diagnosis. Recently, the Information Bottleneck (IB) principle has been extended to GNNs to identify a…

Cited by 0SourceScholar