  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.
