3 papers
eess.SP2025
A brain-inspired generative model for EEG-based cognitive state identification
Bin Hu, Zhi-Hong Guan
This article proposes a brain-inspired generative (BIG) model that merges an impulsive-attention neural network and a variational autoencoder (VAE) for identifying cognitive states…
cs.SD2025
RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis
Yu-Xuan Wu, Ziyan Huang, Bin Hu +1
This article proposes a robust brain-inspired audio feature extractor (RBA-FE) model for depression diagnosis, using an improved hierarchical network architecture. Most deep learni…
cs.NE2025
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks
Junyan Li, Bin Hu, Zhi-Hong Guan
Cross-subject variability in EEG degrades performance of current deep learning models, limiting the development of brain-computer interface (BCI). This paper proposes ISAM-MTL, whi…