arXiv:2606. 05855v1 Announce Type: cross Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals.
By Zhihao Zhou, Weishan Ye, Li Zhang, Gan Huang, Zhen Liang
Neural State Prediction (NSP) is a latent‑predictive framework designed to curb shortcut learning in EEG foundation models. By using a target encoder updated with an exponential moving average, identity residualization, and topology‑separated context, NSP constrains both the prediction target and the available context. Trained on 2.2 million EEG segments, NSP outperforms baselines on 14 datasets in the EEG‑FM‑Bench, achieving 63.94 % macro balanced accuracy.
By Kieren Yu, Ziyang Liu, Chang Huang, Jintai Chen, Kaishun Wu
NextMe-800 is an approximately 800‑hour first‑person video dataset collected from a single volunteer over 126 days, featuring 1 Hz images, gaze, and audio. The data are captioned at five hierarchical abstraction levels—from atomic actions to major activities—enabling personalized action anticipation as an open‑vocabulary K‑step sequence prediction task. The authors also introduce NextAct, a 1,500‑point benchmark that combines NextMe‑800 with the multi‑person EgoLife dataset, and evaluate models using an embedding‑based soft edit distance to assess how well personal behavior can be anticipated across abstraction levels and prediction horizons.
By Zhaoxu Meng, Yiming Sun, Mingyuan Gao, Jiachang Zhang, Zhuhan Dai, Yipeng Du, Zheng Lian, Jian-Qiao Zhu
The paper introduces DSSM-CRF, an audio‑only architecture for conversational speech emotion recognition that separates cross‑speaker contextual influence from within‑speaker emotion evolution. It uses bidirectional state‑space models to encode fused self‑supervised speech representations at both frame and dialogue scales, then orders each speaker’s utterances into an independent dynamic conditional random field chain. The model achieves state‑of‑the‑art performance on IEMOCAP and MELD, with complementary gains from speaker‑wise factorization and CRF modeling.
By Guan-Hua Wen, Hou-Chiang Tseng, Kuan-Yu Chen
The study examines whether personalized latent dynamics can reveal clinically relevant differences even when predictive accuracy is similar. Using a lightweight CNN–Transformer EEG foundation model pretrained on the Temple University EEG Corpus, the authors map EEG representations to a shared latent-state space and fit sparse multinomial logistic transition distributions for each of 198 subjects (99 epilepsy, 99 non‑epilepsy). Results show that epilepsy subjects exhibit denser learned dependency structures at k=4 and k=6, while held‑out log‑likelihood and next‑state prediction are nearly identical between groups, indicating that similar predictive fit does not imply similar learned dynamics.
By Rita Huan-Ting Peng, Nhat Bui
arXiv:2610.27603v1 Announce Type: new
Abstract: Empathetic interaction involves two distinguishable capacities: representing another person's emotional state (cognitive empathy) and generating an app...
By Samhita Bollepally, Takashi Yamauchi, Marissa West