1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
AM-MTEEG: Multi-task EEG classification based on impulsive associative memory
Junyan Li, Bin Hu, Zhi-Hong Guan
Electroencephalogram-based brain-computer interface (BCI) has potential applications in various fields, but their development is hindered by limited data and significant cross-indi…