Background: In most existing EEG-based emotion recognition studies, the spatial configuration of electroencephalography (EEG) electrodes is not adequately preserved, and conventional single-scale convolutional neural networks (CNNs) have limited ability to capture both local and global spatial patterns. To address these limitations, an EEG emotion recognition framework integrating azimuthal equidistant projection (AEP) with a multiscale residual temporal network (MRTNet) is proposed. Methods: Differential entropy (DE) features are first extracted, and the three-dimensional electrode coordinates are projected onto a two-dimensional plane to construct AEP-based DE (AEP-DE) feature maps that preserve the spatial topology of the electrode layout. MRTNet then combines multiscale depthwise separable convolutions with a long short-term memory (LSTM) network to extract spatial features at multiple receptive-field scales and model temporal dependencies across EEG segments. Results: Experimental results show that MRTNet achieves average accuracies of 95.1% for four-class classification on the Database for Emotion Analysis using Physiological Signals (DEAP) and 93.1% for three-class classification on the SJTU Emotion EEG Dataset (SEED). Conclusions: Grad-CAM visualizations further indicate that the model responses are concentrated in spatially localized frontal and temporal regions, providing qualitative support for the spatial interpretability of the proposed method.

Background: Automatic sleep staging is fundamental to sleep architecture analysis and the diagnosis of sleep disorders. Sleep staging requires jointly modeling complementary information across modalities, multi-scale physiological features, and temporal relationships across consecutive epochs. In target epoch prediction from consecutive epochs, non-target epochs primarily provide contextual information, but their ground-truth labels remain underused as auxiliary supervision. Methods: We propose CSPT-SleepNet, an automatic sleep staging framework integrating center-driven sequence supervision with multimodal pyramid token encoding. A Multimodal Pyramid Token Encoder extracts hierarchical multi-scale features from electroencephalogram, electrooculogram, and electromyogram signals, followed by intra-modal, cross-modal, and inter-epoch modeling for center-epoch classification. During training, the center epoch receives primary classification supervision, while non-center epochs receive weaker auxiliary supervision based on their ground-truth labels, providing stage-informed contextual support for the prediction target. Results: In subject-independent cross-validation on Sleep-EDF-20, Sleep-EDF-78, and ISRUC-S3, CSPT-SleepNet achieved accuracy (ACC)/macro F1 (MF1) scores of 87.93%/83.53%, 85.81%/80.87%, and 84.06%/82.86%, respectively. In a separate controlled comparison on Sleep-EDF-20, center-driven sequence supervision improved ACC and MF1 by 0.60 and 0.91 percentage points, respectively, over equal-weight sequence supervision. The full model has 3.418 million trainable parameters and requires 397.10 million floating-point operations for an input sequence of nine 30-s epochs. Conclusions: CSPT-SleepNet provides an effective framework for center-epoch sleep staging from consecutive multimodal inputs. Nevertheless, N1 recognition remains challenging, with F1 scores ranging from 55.3% to 62.7% across the three datasets.