A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks

A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks

Blog Article

Rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is a promising target detection technique by using Christmas Tree electroencephalogram (EEG) signals.However, existing deep learning approaches seldom considered dependencies of multi-scale temporal features and discriminative multi-view spectral features simultaneously, which limits the representation learning ability of the model and undermine the EEG classification performance.In addition, recent transfer learning-based methods generally failed to obtain transferable cross-subject invariant representations and commonly ignore the individual-specific information, leading to the poor cross-subject transfer performance.

In response to these limitations, we propose a cross-scale Transformer and triple-view attention based domain-rectified transfer learning (CST-TVA-DRTL) for the RSVP classification.Specially, we first develop a cross-scale Transformer (CST) to extract multi-scale temporal features and exploit the dependencies of different scales features.Then, a triple-view attention (TVA) is designed to capture spectral features from triple views of multi-channel time-frequency images.

Finally, a domain-rectified transfer learning (DRTL) framework is proposed to simultaneously obtain transferable domain-invariant representations and Ringette Pant Cover untransferable domain-specific representations, then utilize domain-specific information to rectify domain-invariant representations to adapt to target data.Experimental results on two public RSVP datasets suggests that our CST-TVA-DRTL outperforms the state-of-the-art methods in the RSVP classification task.The source code of our model is publicly available in https://github.

com/ljbuaa/CST_TVA_DRTL.