A multi-source classification framework with invariant representation reconstruction for dual-target RSVP-BCI tasks in cross-subject scenario - Chen, Hongying; Wang, Dan; Xu, Meng; Chen, Jiaming; Zhang, Yueqi; Chen, Yuanfang (Neurocomputing (Amsterdam), 2025)
The Rapid Serial Visual Presentation (RSVP) is a widely used paradigm for target detection tasks in Brain-Computer Interface (BCI) by decoding Electroencephalogram (EEG) signals. One major issue concerns the time-consuming calibration in cross-subject scenarios, which worsens in dual-target RSVP-BCI tasks. A new method is desperately needed to detect two targets further with less calibration time. This paper proposed a novel framework named Cross-subject Invariant Representation Extraction-Targeted Stacked Convolutional Autoencoder (CS-IRE-TSCAE) based on reconstructing the invariant representation. After filtering the source subjects, the CS-TSCAE alleviates the subject-dependent effect by reconstructing the invariant representation generated by CS-IRE. It was validated on the ERP datasets from the BCI Controlled Robot Contest 2022. The experimental result showed that CS-IRE-TSCAE obtained the highest Recall, F1 and Average ACC with significant differences both in subject-dependent and inter-subject experiments. It demonstrated that CS-IRE-TSCAE achieved a higher classification performance for dual-target RSVP with less calibration time. Our framework drives the application development of target detection in RSVP-BCI by facilitating multiple target detection in cross-subject scenarios, which has practical significance, especially in fast-deployment scenarios.