Cognitive Neurodynamics

Portable EEG headsets capture far fewer channels than lab-grade systems, limiting emotion decoding accuracy. Researchers at Xiamen University of Technology developed the Spatiotemporal Spectral Asymmetric Fusion Network (STSANet), which explicitly models nonlinear hemispheric lateralization between homologous frontotemporal electrodes. On the SEED benchmark and a self-collected dataset, STSANet achieved 86.80% and 87.43% accuracy, respectively. The team also confirmed consistent spectral energy distributions between the portable Xmuse and the professional-grade Enobio, suggesting the approach could work on consumer-grade hardware.

A team at the Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, has built a hybrid brain-computer interface (BCI) that combines surface electromyography (sEMG) with steady-state visual evoked potentials (SSVEP), encoding 480 targets with 120 flicker frequencies and four hand gestures. In online experiments it achieved a mean classification accuracy of 84.55 ± 7.23% and a mean information transfer rate (ITR), a key measure of practical BCI performance, of 260.07 ± 30.41 bits/min. Its command set matches the largest target counts in existing systems, and the authors place its performance among the top, offering a technical reference for large-command-set BCIs. The work appeared in Cognitive Neurodynamics on September 7, 2026.

Researchers proposed EFS-Net, an end-to-end network that aligns fast EEG activity with slower fNIRS haemodynamic signals through temporal, spatial and cross-modal branches. In subject-specific leave-one-session-out validation, the model reached 77.71% accuracy on a word-generation dataset and 81.69% on a mental-arithmetic dataset.