The study examined 106 depressed adolescents, 67 with non‑suicidal self‑injury (NSSI) and 39 without, using EEG microstate analysis during social pain, physical pain, and rest. A deep sequence model distinguished NSSI participants from controls with 68.55% accuracy, revealing weakened transitions between microstates linked to emotion/interoception (MS3) and action preparation (MS5) during social pain. Time‑resolved analyses showed altered recruitment patterns of these microstates in NSSI adolescents, indicating disrupted emotion‑action coupling.
arXiv:2609.24324v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging du...
arXiv:2606. 00180v1 Announce Type: cross Abstract: Deep learning-based Major Depressive Disorder (MDD) detection using Electroencephalography (EEG) is fundamentally constrained by the "small-sample dilemma.
arXiv:2609.16247v1 Announce Type: new Abstract: Large language models sometimes behave in ways resembling human emotional responses, and recent work has identified internal representations that may e...
Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging due to their high temporal complexity, substantial...
arXiv:2606. 07707v1 Announce Type: new Abstract: Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that oversimplifies the continuous, fluid, and co-occurring nature of human affect.
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...
arXiv:2609.09186v1 Announce Type: new Abstract: Classification models based on resting-state functional magnetic resonance imaging (rs-fMRI) often show lower performance at imaging sites not included...
The paper investigates how EEG embeddings from signal processing and foundation models encode trait and state information. Using a hierarchy metric and kNN purity on a BCI dataset, it finds that most embeddings form a macro‑trait hierarchy with micro‑states inside, which hampers state discrimination. Normalizing embeddings reduces trait leakage and improves state classification accuracy by about 10%, and fine‑tuning shifts the hierarchy toward macro‑state organization, enhancing decoding performance.
arXiv:2607. 04139v1 Announce Type: new Abstract: Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization.
arXiv:2608. 16029v1 Announce Type: new Abstract: Group Independent Component Analysis (gICA) is widely used to decompose high-dimensional functional MRI data into interpretable brain networks.
The paper introduces Dual-Path Spatiotemporal Contrastive Learning (DP-STCL) for cross-subject EEG emotion recognition, using two complementary encoders—Temporal-to-Spatial and Spatial-to-Temporal—to model EEG data under different integration orders. It enforces consistency of high-level emotion representations across these paths and across subjects through three complementary relations, enhancing stability and discriminability. A LoRA-based few-shot adaptation strategy is added to handle limited labeled data from target subjects, and experiments on SEED, SEED-IV, SEED-V, and FACED datasets show DP-STCL outperforming existing methods.
Understanding how brain regions interact to support cognitive tasks requires characterizing directed causal influences among them, known as effective connectivity (EC). As inter-regional coupling chan...