The first author of the paper is Kangning Wang, title：“A Fine-Grained Domain Adaptation Method for Cross-Session Vigilance Estimation in SSVEP-Based BCI”

Abstract：

Brain-computer interface (BCI), a direct communication system between the hu-man brain and external environment, can provide assistance for people with disa-bilities. Vigilance is an important cognitive state and has a close influence on the performance of users in BCI systems. In this study, a four-target BCI system for cursor control was built based on steady-state visual evoked potential (SSVEP) and twelve subjects were recruited and carried out two long-term BCI experi-mental sessions, which consisted of two SSVEP-based cursor-control tasks. During each session, electroencephalogram (EEG) signals were recorded. Based on the labeled EEG data of the source domain (previous session) and a small amount of unlabeled EEG data of the target domain (new session), we developed a fine-grained domain adaptation network (FGDAN) for cross-session vigilance estimation in BCI tasks. In the FGDAN model, the graph convolution network (GCN) was built to extract deep features of EEG. The fined-grained feature alignment was proposed to highlight the importance of the different channels fig-ured out by the attention weights mechanism and aligns the feature distributions between source and target domains at the channel level. The experimental results demonstrate that the proposed FGDAN achieved a better performance than the compared methods and indicate the feasibility and effectiveness of our methods for cross-session vigilance estimation of BCI users.

The first author of the paper is Che Liu, title：“RoBrain: Towards Robust Brain-to-Image Reconstruction via Cross-Domain Contrastive Learning”

Abstract：

With the development of neuroimaging technology and deep learning methods, neural decoding with functional Magnetic Resonance Imaging (fMRI) of human brain has attracted more and more attention. Neural reconstruction task, which intends to reconstruct stimulus images from fMRI, is one of the most challenging tasks in neural decoding. Due to the instability of neural signals, trials of fMRI collected under the same stimulus prove to be very different, which leads to the poor robustness and generalization ability of the existing models. In this work, we propose a robust brain-to-image model based on cross-domain contrastive learning. With deep neural network (DNN) features as paradigms, our model can extract features of stimulus stably and generate reconstructed images via DCGAN. Experiments on the benchmark Deep Image Reconstruction dataset show that our method can enhance the robustness of reconstruction significantly.