CASIA OpenIR  > 脑图谱与类脑智能实验室  > 神经计算与脑机交互
TFF-Former: Temporal-frequency fusion transformer for zero-training decoding of two BCI tasks
Li XJ(李叙锦)1,2; Wei W(魏玮)1; Qiu S(邱爽)1,2; He HG(何晖光)1,2
2022-10
Conference NameProceedings of the 30th ACM International Conference on Multimedia
Conference DateOctober 10 - 14, 2022
Conference PlaceLisboa Portugal
Abstract

Brain-computer interface (BCI) systems provide a direct connection between the human brain and external devices. Visual evoked BCI systems including Event-related Potential (ERP) and Steady-state Visual Evoked Potential (SSVEP) have attracted extensive attention because of their strong brain responses and wide applications. Previous studies have made some breakthroughs in within-subject decoding algorithms for specific tasks. However, there are two challenges in current decoding algorithms in BCI systems. Firstly, current decoding algorithms cannot accurately classify EEG signals without the data of the new subject, but the calibration procedure is time-consuming. Secondly, algorithms are tailored to extract features for one specific task, which limits their applications across tasks. In this study, we proposed a Temporal-Frequency Fusion Transformer (TFF-Former) for zero-training decoding across two BCI tasks. EEG data were organized into temporal-spatial and frequency-spatial forms, which can be considered as two views. In the TFF-Former framework, two symmetrical Transformer streams were designed to extract view-specific features. The cross-view module based on the cross-attention mechanism was proposed to guide each stream to strengthen common representations of features across EEG views. Additionally, an attention-based fusion module was built to fuse the representations from the two views effectively. The mean mask mechanism was applied to adaptively decrease redundant EEG tokens aggregation for the integration of common representations. We validated our method on the self-collected RSVP dataset and benchmark SSVEP dataset. Experimental results demonstrated that our TFF-Former model achieved competitive performance compared with models in each of the above paradigms. It can further promote the application of visual evoked EEG-based BCI system.

Sub direction classification脑机接口
planning direction of the national heavy laboratory人机混合智能
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57337
Collection脑图谱与类脑智能实验室_神经计算与脑机交互
Corresponding AuthorHe HG(何晖光)
Affiliation1.Research Center for Brain-inspired Intelligence & National Laboratory of Pattern Recognition, CASIA
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li XJ,Wei W,Qiu S,et al. TFF-Former: Temporal-frequency fusion transformer for zero-training decoding of two BCI tasks[C],2022.
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