activity
20182026
most citedDynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces

6 citations · 19 across the 18 of their papers we have counts for

collaborators
Showing 2023Show all

5 papers · 1 filter

cs.CV20233 cited

MindGPT: Interpreting What You See with Non-invasive Brain Recordings

Jiaxuan Chen, Yu Qi, Yueming Wang +1

Decoding of seen visual contents with non-invasive brain recordings has important scientific and practical values. Efforts have been made to recover the seen images from brain sign…

cs.HC2023

A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training

Hanwen Wang, Yu Qi, Lin Yao +3

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. En…

cs.NE20234 cited

ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks

Jiangrong Shen, Qi Xu, Jian K. Liu +3

Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low powe…

cs.HC2023

Decoding Chinese phonemes from intracortical brain signals with hyperbolic-space neural representations

Xianhan Tan, Junming Zhu, Jianmin Zhang +2

Speech brain-computer interfaces (BCIs), which translate brain signals into spoken words or sentences, have shown significant potential for high-performance BCI communication. Phon…

cs.NE20233 cited

LaSNN: Layer-wise ANN-to-SNN Distillation for Effective and Efficient Training in Deep Spiking Neural Networks

Di Hong, Jiangrong Shen, Yu Qi +1

Spiking Neural Networks (SNNs) are biologically realistic and practically promising in low-power computation because of their event-driven mechanism. Usually, the training of SNNs…