Xidian University

Researchers report FRED, a principled EEG-fNIRS fusion framework for imagined handwriting decoding, posted to arXiv as a preprint on August 4, 2026 and not yet peer reviewed. Imagined handwriting offers a temporally rich paradigm for non-invasive neural decoding, and EEG-fNIRS fusion promises complementary neural information, but fusion is typically heuristic and lacks principled treatment of frequency-band redundancy. FRED builds frequency-decorrelated temporal ensembles for imagined handwriting decoding. The ensemble reaches 0.8076/0.7242/0.7492 accuracy on the public/private/overall test partitions without test-set adaptation or output constraints, and the complete system reaches 0.8498/0.7718/0.7952, ranking fourth on the private split. A modality audit finds fNIRS-only decoding at chance (0.2511 overall), while adding fNIRS to EEG changes accuracy by only +0.0025.

Researchers at Xidian University built DisCo-Former, a Transformer framework for single-trial rapid serial visual presentation (RSVP) EEG decoding with three components guided by neurophysiological priors, then found its attention consistently collapsed: attention maps went nearly uniform and value-projection weights shrank toward 0. Stripping out the Transformer encoder left DisCo-MLP, a pure multilayer perceptron that matched or beat the Transformer version across two datasets and three evaluation regimes, with within-subject mean AUCs of about 0.94 to 0.98. For RSVP-EEG, the authors argue, modeling the signal's structure matters more than architectural complexity. The study was published in the International Journal of Neural Systems on April 10, 2026.

Researchers propose a function predefined convolutional neural network (FPCNN) for decoding spontaneous EEG in brain-computer interfaces. Its learnable function predefined convolution (FPC) layer searches for the key spatial-frequency parameters of spontaneous EEG so the parameters carry clear physical meaning, and it builds trainable orthogonal detectors on the FPC to capture complex phase-varying signals. On three spontaneous EEG datasets, FPCNN outperformed state-of-the-art methods by 2.09%, 3.08% and 3.41%, with single-round training and testing taking just 67.96 and 19.36 seconds on non-GPU hardware, which the authors say makes it suited to EEG processing in diverse environments.