Frequency-Decorrelated Temporal Ensembles for EEG--fNIRS Imagined-Handwriting Decoding
Imagined handwriting offers a temporally rich paradigm for non-invasive neural decoding, yet reliable recognition across unseen participants remains difficult because scalp EEG is noisy and internally generated stroke sequences vary across individuals. The Multimodal Brain-Computer Interface Grand Challenge provides synchronized EEG and fNIRS for four-class subject-independent handwriting-trajectory classification. We propose FRED, a task-adapted system that models imagined handwriting as a multi-second motor sequence and trains a compact multi-scale temporal network on three complementary EEG frequency views. With three seeds per view, cross-band members produce substantially less-correl...
| Authors | Xiao Fan, Hongbin Guo, Yubo Han et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 August 2026 |
| Last updated at source | 26 August 2026 |
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