SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs
<|reference_start|>SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs: Hybrid motor imagery brain-computer interfaces (MI-BCIs), which integrate both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperform those based solely on EEG. However, simultaneously recording EEG and fNIRS signals is highly challenging due to the difficulty of colocating both types of sensors on the same scalp surface. This physical constraint complicates the acquisition of high-quality hybrid signals, thereby limiting the widespread application of hybrid MI-BCIs. To facilitate the acquisition of hybrid EEG-fNIRS signals, this study proposes the spatio-temporal controlled diffusion model (SCDM) as a framework for cross-modal generation from EEG to fNIRS. The model utilizes two core modules, the spatial cross-modal generation (SCG) module and the multi-scale temporal representation (MTR) module, which adaptively learn the respective latent temporal and spatial representations of both signals in a unified representation space. The SCG module further maps EEG representations to fNIRS representations by leveraging their spatial relationships. Experimental results show high similarity between synthetic and real fNIRS signals. The joint classification performance of EEG and synthetic fNIRS signals is comparable to or even better than that of EEG with real fNIRS signals. Furthermore, the synthetic signals exhibit similar spatio-temporal features to real signals while preserving spatial relationships with EEG signals. Experimental results suggest that the SCDM may represent a promising paradigm for the acquisition of hybrid EEG-fNIRS signals in MI-BCI systems.<|reference_end|>
@article{li2024scdm:,
    title={SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal
  Generation in MI-BCIs},
    author={Yisheng Li and Shuqiang Wang},
    journal={arXiv preprint arXiv:2407.04736},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04736},
    primaryClass={eess.SP cs.AI cs.LG}
}