Hybrid interfaces combine neuroimaging modalities so that one covers what another misses. For the pairing of electroencephalography and functional near-infrared spectroscopy, the obstacle is that fNIRS localises activity well but carries a delayed haemodynamic response, while EEG offers millisecond timing with limited spatial detail, leaving the two streams misaligned in both time and space. EFS-Net addresses that mismatch with a multi-scale spatio-temporal fusion architecture built on spatial calibration.
The network runs three complementary branches: a multi-scale temporal convolution branch that captures rapidly changing cortical electrophysiological features in EEG, an EEG spatial branch that constructs latency-compensated cortical topographies to accommodate the slower haemodynamic response, and a spatially calibrated fNIRS branch that dynamically fuses spatial feature maps with their EEG counterparts to produce temporally aligned, spatially enhanced neural representations. Evaluation used subject-specific leave-one-session-out cross-validation on the public Word Generation and Mental Arithmetic datasets, and visualisations indicate the alignment strategy recovers realistic cortical spatial distributions. The authors are based at East China University of Science and Technology, the University of Essex, and partner institutions.