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Muscle activity can swamp scalp EEG across a broad frequency range and bury the neural information that analysis and brain-computer interfaces rely on. Researchers built P2P Trans, a conditional adversarial denoiser that combines a U-shaped encoder-decoder, multi-head self-attention at the bottleneck, cross-attention-guided skip fusion and a PatchGAN discriminator. On synthetic data built from EEGdenoiseNet across 10 signal-to-noise levels from -7 to 2 dB, it beat 5 convolutional encoder-decoder baselines with the best mean correlation coefficient (0.8645) and lowest mean spectral error (0.3596), with the margin widening as contamination grew. It did not lead on pointwise waveform reconstruction, pointing to a trade-off between suppressing spectral noise and preserving the waveform. The work is a preprint and has not been peer reviewed.

EEG is easily contaminated by blinks, muscle activity and heartbeats, which can drag down how accurately a brain-computer interface reads intent. Researchers from the School of Automation and the School of Software at Northwestern Polytechnical University in Xi'an, northwestern China, built Diff-ADN, a single-channel denoiser that first reconstructs the signal according to contamination severity, then applies one diffusion-guided residual correction. It handles ocular, muscular, cardiac and mixed artifacts, reaching mean Pearson correlations with clean EEG of 0.839 to 0.936 at contamination levels from -7 to +2 dB. Across 4 public motor-imagery datasets, removing cardiac artifacts recovered 4.88 to 5.59 percentage points of decoding accuracy, up to 21.90 points in a single case, and a 2-second segment took 12.52 to 17.08 ms on the tested CPU. Better waveform reconstruction did not always translate into equal gains in decoding accuracy.

Decoding individual words from scalp EEG and MEG is hard because word-level neural evidence is weak, spread out in time, and entangled with acoustic, lexical and semantic structure. Deep learning can already decode language from intracranial recordings, but extending that to non-invasive recordings remains unsolved. Hierarchical Dynamic Neural Decoding (HDND), proposed by Yueyang Li, Shuran Chen, Wai Ting Siok and Nizhuan Wang, treats word decoding as step-by-step refinement rather than one-shot label retrieval. Across seven EEG and MEG datasets in English, Dutch, Mandarin and Cantonese, covering nine listening, reading and reading-aloud conditions, HDND beat the matched contextual baseline on participant-averaged balanced Top-10 accuracy in all nine and had the highest mean of all compared methods in eight. The work is a preprint and has not been peer reviewed.

Researchers recorded single neurons in the motor cortex of paralyzed people with intracortical brain-computer interface implants while they watched virtual human hands, robotic hands and hand-like dots perform grasps. The more human-like the effector, the stronger the motor cortex response, both across the neural population and in individual neurons, and the effect tracked participants' own sense of human-likeness. When users attempted hand movements themselves, however, motor cortex activity stayed largely the same regardless of what was on screen, suggesting that intuitive control of external devices may not depend much on whether the device looks human.

Researchers in Portugal had seven people with chronic stroke imagine arm movements while an EEG-based brain-computer interface gave some of them real-time feedback and others sham feedback; both groups also received four weeks of standard physiotherapy. Patients receiving real feedback improved beyond the minimal clinically important difference on at least one functional measure, most clearly grip strength, and their brain activity became more symmetrical between hemispheres. As a small case series, the study shows the method is feasible, and its results are preliminary.

Portable EEG headsets have few channels and capture limited cortical information, making emotion decoding difficult. A team at Xiamen University of Technology in the eastern Chinese province of Fujian developed the Spatiotemporal Spectral Asymmetric Fusion Network (STSANet), which models nonlinear hemispheric lateralization between homologous frontotemporal electrodes. It reached 86.80% accuracy on the public SEED dataset and 87.43% on a self-collected dataset. The team also found consistent spectral energy distributions between the portable Xmuse headset and the professional-grade Enobio, suggesting the method could run on consumer EEG hardware. The work is a preprint and has not been peer reviewed.

A team at Chongqing University's School of Electrical Engineering in southwestern China built MTANet, an end-to-end neural network for decoding motor imagery EEG. An EEG inception attention module extracts and weights coupled time-frequency-spatial features, and a parallel temporal attention module runs several temporal convolutional networks over weighted segments of different feature sequences. In offline tests on three public datasets, MTANet outperformed seven existing models, with average accuracies of 89.47% on BCI Competition IV dataset IIb, 83.39% on dataset IIa and 77.06% on BCI Competition III dataset IIIa. It has not been tested in an online BCI setting.

Pretrained EEG foundation models are increasingly seen as general-purpose encoders for brain-computer interfaces, but recent benchmarks disagree on how well they transfer. Evaluated under a validation-locked protocol on the four-class BCI Competition IV-2a motor imagery dataset, two leading models, LaBraM and CBraMod, underperformed every supervised comparator in every configuration, including validation-selected fine-tuning. A key confound emerged: foundation models and task-specific decoders are usually evaluated with different input pipelines. When three supervised architectures were retrained on the broadband input the foundation models use, accuracy moved in opposite directions (ATCNet gained 0.078, EEG Conformer lost 0.088), and none of the changes was significant after multiple-comparison correction at n = 9. Measuring the foundation-model gap against a single comparator may therefore be unreliable. The work is a preprint and has not been peer reviewed.

The study introduces a model designed to reduce the impact of differences between participants' EEG signals on motor imagery classification. It combines comprehensive deep feature representation with domain adaptation to learn information shared across participants. Tests on four benchmark datasets produced average accuracies of 84.34%, 89.59%, 73.32%, and 87.75%. The authors report better performance than the state-of-the-art deep domain adaptation models included in their comparisons. Ablation experiments also assessed the two core modules. These findings concern cross-subject classification on datasets, not demonstrated online BCI control or rehabilitation benefits.

When a person views an image, the brain leaves measurable electrical and magnetic traces. Most existing decoders match those signals to a single global image embedding. This study instead aligns them with all patch tokens from an intermediate layer of a vision model, using a lightweight Residual Patch Adapter (RPA). On the public THINGS-EEG2 dataset it reached Top-1 accuracy of 95.4% within-subject and 35.5% cross-subject; on THINGS-MEG the figures were 65.2% and 6.7%. The authors report state-of-the-art results on both. The study is a preprint and has not been peer reviewed.