The paper introduces SPD-MetaFormer, an attention‑free architecture for decoding brain signals from small datasets. It shows that existing attention‑based models on the SPD manifold (MAtt and GBWAtt) learn nearly uniform weights, and that replacing learned weights with uniform ones does not degrade performance. SPD-MetaFormer uses uniformly weighted Fréchet aggregation with a geodesic residual and a shared spectral feedforward map, achieving competitive results on three EEG benchmarks while maintaining SPD‑valued token states until classification.
By Zhida Wang, Wei Lyu, Guo Yu, Sui Tang
arXiv:2609.36971v1 Announce Type: new
Abstract: Cross-subject EEG-to-image retrieval requires a neural represen- tation trained on source subjects to remain aligned with a visual embedding space for...
By Salini Yadav, Taveena Lotey, Micka\"el Coustaty, Pravendra Singh, Partha Pratim Roy
The paper introduces the Residual Patch Adapter (RPA), a lightweight module that aligns EEG and MEG signals with all patch tokens from an intermediate layer of a Vision Transformer, rather than a single global embedding. Ablation studies show that retaining the full set of patch tokens is crucial for effective alignment, while the CLS token adds little value. Using RPA, the authors achieve state‑of‑the‑art Top‑1 accuracies on THINGS‑EEG2 and THINGS‑MEG datasets, and demonstrate that the approach generalizes to other brain encoders and can be swapped with various neural network backbones.
By Yuhui Jin, Yonghao Song, Bingchuan Liu
The paper introduces STD, a hierarchical token pruning framework for Large Vision‑Language Models that aligns pruning strategies with the functional roles of different network stages. By using high‑frequency spectral analysis in shallow layers, Gaussian‑smoothed attention in intermediate layers, and a stability‑adaptive trigger in deep layers, STD preserves essential visual information while aggressively reducing token counts. Experiments demonstrate that STD outperforms existing pruning methods, achieving up to 94.4% token reduction and a 3.9× speed‑up on LLaVA‑NeXT‑7B.
By Shuo Zhang, Jintao Tong, Yixiong Zou, Yuhua Li, Ruixuan Li
The paper introduces ‘One Model for All’, a universal pre‑training framework that tackles EEG‑based emotion recognition across diverse datasets and paradigms. It decouples learning into a univariate self‑supervised contrastive pre‑training stage using a Unified Channel Schema, followed by a multivariate fine‑tuning stage that employs an Adaptive Resampling Transformer and a Graph Attention Network to model spatio‑temporal dependencies. Experiments demonstrate state‑of‑the‑art performance on within‑subject benchmarks (SEED 99.27%, DEAP 93.69%, DREAMER 93.93%) and superior cross‑dataset transfer, with ablation studies highlighting the critical role of the GAT module.
By Xiang Li, You Li, Yazhou Zhang
arXiv:2605.23137v3 Announce Type: replace-cross
Abstract: Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured...
By Jiahe Meng, Weiming Zeng, Yueyang Li, Bo Chai, Hongjie Yan, Zhiguo Zhang, Wai Ting Siok, Nizhuan Wang
arXiv:2610.15341v1 Announce Type: cross
Abstract: The inherent variability of fMRI responses across trials encourages a generative view of the stimulus-to-response mapping. Prior generative encoders...
By Bui Van Hung, Dang Thanh Vu, Hoang-Son Vo, Mai Duc Minh Huy, Mai Tien Dung, Soo-Hyung Kim
arXiv:2608. 02070v2 Announce Type: replace-cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.
By Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu
arXiv:2602. 10528v2 Announce Type: replace-cross Abstract: We propose a novel swap-adversarial framework that mitigates high inter-subject variability and the high-dimensional low-sample-size problem in electrocorticography (ECoG) data.
By Seongwon Jin, Hanseul Choi, Sunggu Yang, Sungho Park, Jibum Kim
arXiv:2608. 02070v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.
By Zhu Chen, Dingkun Liu, Yuheng Chen, Dongrui Wu
The paper introduces Adaptive Anisotropic Attention (AAA), a method that splits self‑attention into temporal and spatial paths for axis‑structured signals like EEG. A learned gate combines the two paths for each token, and the resulting AXON model outperforms dense attention baselines on six EEG tasks and shows transfer to audio spectrograms. The study demonstrates that aligning attention with the natural axes of structured data provides a beneficial inductive bias.
By Mahir Jain, Parshva Runwal, Aditya Ray Mishra, Arvasu Kulkarni, Sandeep Singh, Siddharth Panwar
The paper introduces QK Product Steering, a data‑free, training‑free method that edits the query‑key product in vision‑language models to reduce object hallucination. By suppressing a few dominant singular modes in selected middle layers and mapping the edited product back to query weights, the approach lowers hallucination rates without affecting inference cost. Experiments on three GQA‑based VLMs show a 4.0% average reduction in CHAIR$_s$, with the effect localized to symmetric mutual‑attention channels.
By Karn Tiwari, Varnith Chordia, Prathosh A P