activity
20242026
most citedCross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

3 citations · 5 across the 7 of their papers we have counts for

collaborators
Showing 2026Show all

6 papers · 1 filter

eess.SP2026

Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography

Yuanyuan Zhang, Yida Zhang, Jiahui Li +6

Ballistocardiography (BCG) is promising for unobtrusive long-term blood pressure (BP) monitoring in laboratory settings, but traditional BCG signals are vulnerable to the variation…

cs.AI2026

EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses

Jiahui Li, Ruili Fang, Zishuai Liu +5

Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence. Existing diagnosis-prediction benchmarks are poorly suited to this setting: they re…

cs.CV2026

LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition

Zishuai Liu, Ruili Fang, Jin Lu +1

Human Activity Recognition (HAR) from ambient sensors enables smart-home applications such as health monitoring and assisted living. In realistic deployments, however, sensor event…

cs.LG20263 cited

Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

Taida Li, Yujun Yan, Fei Dou +2

Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This s…

cs.CV2026

LARV: Data-Free Layer-wise Adaptive Rescaling Veneer for Model Merging

Xinyu Wang, Ke Deng, Fei Dou +2

Model merging aims to combine multiple fine-tuned models into a single multi-task model without access to training data. Existing task-vector merging methods such as TIES, TSV-M, a…

cs.CV2026

Achieving Fine-grained Cross-modal Understanding through Brain-inspired Hierarchical Representation Learning

Weihang You, Hanqi Jiang, Yi Pan +3

Understanding neural responses to visual stimuli remains challenging due to the inherent complexity of brain representations and the modality gap between neural data and visual inp…