The Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface
(BCI) is an efficient technology for target retrieval using
electroencephalography (EEG) signals. The performance improvement of
traditional decoding methods relies on a substantial amount of training data
from new test subjects, which increases preparation time for BCI systems.
Several studies introduce data from existing subjects to reduce the dependence
of performance improvement on data from new subjects, but their optimization
strategy based on adversarial learning with extensive data increases training
time during the preparation procedure. Moreover, most previous methods only
focus on the single-view information of EEG signals, but ignore the information
from other views which may further improve performance. To enhance decoding
performance while reducing preparation time, we propose a Temporal-Spectral
fusion transformer with Subject-specific Adapter (TSformer-SA). Specifically, a
cross-view interaction module is proposed to facilitate information transfer
and extract common representations across two-view features extracted from EEG
temporal signals and spectrogram images. Then, an attention-based fusion module
fuses the features of two views to obtain comprehensive discriminative features
for classification. Furthermore, a multi-view consistency loss is proposed to
maximize the feature similarity between two views of the same EEG signal.
Finally, we propose a subject-specific adapter to rapidly transfer the
knowledge of the model trained on data from existing subjects to decode data
from new subjects. Experimental results show that TSformer-SA significantly
outperforms comparison methods and achieves outstanding performance with
limited training data from new subjects. This facilitates efficient decoding
and rapid deployment of BCI systems in practical use.

通过提出一种时间 - 频谱融合变换器与个体适配器相结合的快速连续视觉呈现 (RSVP)- 基于脑机接口 (BCI) 技术，以减少依赖新被试数据的训练时间，提高解码性能。