arXiv:2504. 03707v2 Announce Type: replace-cross Abstract: Emotion recognition is crucial for advancing mental health, healthcare, and technologies such as brain-computer interfaces.
By Md Niaz Imtiaz, Naimul Khan
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
The paper introduces a Cross-Subject Perceived Speech Decoding (CPSD) framework that tackles the challenge of decoding perceived speech from non‑invasive brain recordings across different subjects. CPSD uses a two‑stage training process: first, contrastive learning pre‑trains a source model on multiple subjects to capture shared representations; second, personal specialization fine‑tunes the model for a target subject by extracting consistent components and further training on that subject’s data. A Positional Encoding‑based Spatial Attention (PESA) module is added to remap MEG/EEG data into a standardized reference space, improving cross‑subject consistency. Evaluations on three datasets (Armeni 2022, PKUEEG 2025, Broderick 2018) show that CPSD outperforms baseline methods by more than 6.8%, 15.4%, and 15.8% in Top‑10 accuracy, demonstrating its effectiveness, efficiency, and robustness.
By Aoke Zhang, Bo Wang, Xihong Wu, Heping Cheng, Jing Chen
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: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
LEAD is a gated temporal‑spatial Transformer foundation model designed for EEG‑based Alzheimer's disease detection. It was trained on the world’s largest EEG‑AD corpus of 2,238 subjects and uses a subject‑regularized strategy and medical contrastive learning across 13 datasets. LEAD outperforms existing methods on five downstream AD datasets, achieving the best average ranking across 20 evaluations.
By Yihe Wang, Nan Huang, Nadia Mammone, Marco Cecchi, Xiang Zhang