arXiv:2608. 09088v1 Announce Type: new Abstract: Mixed emotions represent a clinically relevant but still underexplored target for automatic emotion recognition.
arXiv:2608. 15999v1 Announce Type: new Abstract: Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specific feature-extraction pipelines before fusion.
arXiv:2409. 07589v2 Announce Type: cross Abstract: EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces.
arXiv:2606. 10718v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely adopted technique for monitoring brain activity, offering valuable insights into neurological states due to its high temporal resolution and cost-effectiveness.
The paper introduces EmoSpeechBrain, a multimodal emotion recognition framework that fuses EEG and speech signals. It employs differential attention in the EEG encoder to cancel shared noise and an attention-based gating adapter to align modalities and weight their contributions. Experiments on PME4 and EAV datasets show up to 12.9% accuracy improvement over other EEG encoders and surpass unimodal baselines by up to 23.1%.
The paper introduces MSCGC-KAN, a new EEG emotion recognition approach that builds on a pre‑trained CBraMod backbone. It incorporates a structured task head featuring multi‑scale causal graph convolution and Kolmogorov–Arnold feature mapping to better capture multi‑scale emotional dynamics, inter‑channel connectivity, and nonlinear discriminative patterns. Experiments on FACED and SEED‑VII show significant performance gains over a linear baseline, achieving balanced accuracies of 60.66% and 33.27% respectively.
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.
arXiv:2606. 00884v1 Announce Type: cross Abstract: We study cross-subject emotion recognition from EEG, a practically important yet challenging problem in brain-computer interfaces.
arXiv:2602. 06411v2 Announce Type: replace Abstract: EEG-based emotion recognition supports affective brain-computer interfaces and mental health monitoring yet remains challenged by signal complexity, subject variability, and limited interpretability.
arXiv:2606. 30104v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings.
The paper introduces a multimodal emotion recognition framework that combines audio and visual feature extraction with an attention-based fusion strategy. Audio features include Wav2Vec2 embeddings, MFCCs, and statistical acoustic descriptors, fused via a BiLSTM, while video features are extracted using a ResNet50-BiLSTM architecture. A multi-head attention mechanism fuses these modalities, and experiments on MELD and IEMOCAP show significant accuracy and robustness gains, especially in unbalanced data settings.
arXiv:2607. 21402v1 Announce Type: new Abstract: Self-supervised foundation models have recently shown strong potential for electroencephalogram (EEG)-based analysis.
The study introduces two fusion techniques—montage and blending—to combine EEG-derived time‑frequency images for predicting response to repetitive Transcranial Magnetic Stimulation (rTMS) in depression. Using a lightweight custom CNN, the authors evaluated the methods on two datasets (15 and 46 patients) under both segment‑level and subject‑disjoint cross‑validation. While segment‑level validation yielded high accuracies (up to 99.90 % on the primary dataset), subject‑disjoint validation showed poor performance, with AUC values ranging from 0.31 to 0.54 and a best subject‑level result of 0.874 ± 0.183 AUC.