arXiv Machine Learning By Zhenhang Li, Xin Zhou, Hao Deng, Lijun Yin

MindAU: EEG-Conditioned Facial Action Unit Editing via Dual-Stream Manifold Alignment

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arXiv:2607. 00410v1 Announce Type: cross Abstract: Recent brain decoding studies have made substantial progress in reconstructing externally perceived visual content from neural signals.

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arXiv Computer Vision
Oct 7

RPA: Residual Patch-Token Adapter for Image Retrieval from EEG and MEG

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
arXiv Computer Vision
Sep 4

Occlusion-Robust Multimodal Emotion Recognition in VR via Fusion of Facial Images and EMG

The paper presents a method for emotion recognition in virtual reality where head‑mounted displays occlude the upper face. By fusing lower‑face video with electromyography (EMG) signals from the occluded upper face, the authors achieve a 51% macro‑F1 score across seven emotional categories, outperforming image‑only and EMG‑only baselines. A new synchronized multimodal dataset from 20 participants is introduced and will be shared under an ethical‑use agreement.

By Birgit Nierula, Karam Tomotaki-Dawoud, Mert Akguel, Mustafa Tevfik Lafci, David Przewozny, Anna Hilsmann, Peter Eisert, Sebastian Bosse
arXiv AI
Jun 2

UF-AMA: A unified framework for cross-domain emotion recognition via adaptive multimodal alignment

arXiv:2606. 00170v1 Announce Type: cross Abstract: In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions.

By Zheng Wang, Shuo Wang, Junhong Wang