Yan Liang

Yitian Tao, Yan Liang, Luoyu Wang et al.

Decoding neurophysiological signals into language is of great research interest within brain-computer interface (BCI) applications. Electroencephalography (EEG), known for its non-invasiveness, ease of use, and cost-effectiveness, has been a popular method in this field. However, current EEG-to-Text decoding approaches face challenges due to the huge domain gap between EEG recordings and raw texts, inherent data bias, and small closed vocabularies. In this paper, we propose SEE: Semantically Aligned EEG-to-Text Translation, a novel method aimed at improving EEG-to-Text decoding by seamlessly integrating two modules into a pre-trained BART language model. These two modules include (1) a Cross-Modal Codebook that learns cross-modal representations to enhance feature consolidation and mitigate domain gap, and (2) a Semantic Matching Module that fully utilizes pre-trained text representations to align multi-modal features extracted from EEG-Text pairs while considering noise caused by false negatives, i.e., data from different EEG-Text pairs that have similar semantic meanings. Experimental results on the Zurich Cognitive Language Processing Corpus (ZuCo) demonstrate the effectiveness of SEE, which enhances the feasibility of accurate EEG-to-Text decoding.

Chaoxiong Ma, Yan Liang, Huixia Zhang et al.

It is explored that available credible evidence fusion schemes suffer from the potential inconsistency because credibility calculation and Dempster's combination rule-based fusion are sequentially performed in an open-loop style. This paper constructs evidence credibility from the perspective of the degree of support for events within the framework of discrimination (FOD) and proposes an iterative credible evidence fusion (ICEF) to overcome the inconsistency in view of close-loop control. On one hand, the ICEF introduces the fusion result into credibility assessment to establish the correlation between credibility and the fusion result. On the other hand, arithmetic-geometric divergence is promoted based on the exponential normalization of plausibility and belief functions to measure evidence conflict, called plausibility-belief arithmetic-geometric divergence (PBAGD), which is superior in capturing the correlation and difference of FOD subsets, identifying abnormal sources, and reducing their fusion weights. The ICEF is compared with traditional methods by combining different evidence difference measure forms via numerical examples to verify its performance. Simulations on numerical examples and benchmark datasets reflect the adaptability of PBAGD to the proposed fusion strategy.