Cross-subject EEG decoding remains a fundamental challenge due to substantial inter-subject variability in brain activity, which hinders the development of subject-independent EEG models. Despite progress in extracting cross-subject invariant features, existing studies neglect the shared neural responses that arise under similar cognitive or emotional states across individuals, limiting their ability to learn generalized and consistent EEG representations. To address the challenges, we propose State Mamba, a novel spatiotemporal EEG state-space model that explicitly models and aligns neural responses and their spatiotemporal state transitions to learn consistent and generalizable representations across subjects. Innovatively, State Mamba theoretically formulates a multi-channel Mamba architecture that jointly models spatial and temporal brain state transitions, supporting principled analysis of neural responses. To enhance spatiotemporal feature coupling, we introduce the LGANN module, which adopts global-local attention to integrate long- and short-term brain activity into a compact EEG representation. Furthermore, we design two self-supervised pretext tasks to extract consistent neural patterns across subjects: (1) representation alignment to align EEG representation, and (2) pattern alignment to align their transition rules under identical conditions, jointly promoting subject-invariant EEG representations. Extensive experiments on three benchmark datasets, FACED, DEAP, and ISRUC, demonstrate the superior performance of State Mamba in cross-subject emotion and sleep recognition tasks, validating its robust generalization capability.

Class-incremental learning (CIL) enables models to continuously learn new classes while addressing catastrophic forgetting. With the introduction of pre-trained models, new tuning paradigms have emerged for CIL. This paper revisits parameter-efficient fine-tuning (PEFT) methods in the context of incremental learning. Prior studies reveal that PEFT methods’ extended parameters do not directly contribute to semantic perception, limiting performance with significant category and domain gaps. To address this, we propose semantic-oriented visual prompt learning (SVPL), which enhances semantic perception and improves task-specific knowledge extraction. SVPL assigns learnable prompts to each class, using a contrastive group alignment to align prompts to task-specific semantic spaces, thus preserving relationships between old and new knowledge. Additionally, hierarchical semantic delivery allows the semantic transformation of prompt groups from shallow to deep layers to facilitate efficient knowledge mining and enable effective learning of new knowledge. Extensive experimental results on five benchmarks demonstrate the superior performance of our methods.

Wearable-based time series recognition aims to infer behavioral classes based on time series signals collected by sensors. Domain Adaptation (DA) methods have significantly improved the performance on out-of-domain test classification tasks. However, highly imbalanced label distribution in real-world tasks hinders the practicality of DA methods. In this paper, we propose a Multi-Expert Consistency Entropy Minimization (MECEM) method for class-imbalanced domain adaptation. Through the incorporation of a multi-expert committee mechanism, we recalibrate decision boundaries in response to imbalanced source distributions, thereby mitigating bias towards minority classes and promoting fairness in committee decisions. Furthermore, we introduce a time-frequency dynamic augmentation mechanism, which simultaneously addresses domain shift on the target domain and dynamically adapts the augmentation library for the classification task. The MECEM method facilitates the propagation of consistency entropy during backpropagation, conveying both the reliability of pseudo-labels and the classification task relevance of augmentation techniques. Experiments across various tasks on activity recognition and Parkinson’s tremor grading demonstrate the superiority of our method.

Electroencephalogram (EEG)-based sleep stage classification (SSC) identifies human sleep phases from EEG signals. With the integration of deep learning frameworks, automatic SSC has achieved notable accuracy. However, current SSC approaches encounter significant challenges: 1) Data challenge. The high costs associated with collecting and annotating sleep EEG signals result in a limited number of training samples, complicating supervised training for deep models. 2) Knowledge challenge. Incorporating clinical and human insights related to sleep into models for SSC remains challenging. To address these challenges, we propose Wafformer, a novel self-supervised framework to extract knowledge-enhanced sleep representation from EEG signals. Specifically, Wafformer leverages the Transformer architecture, employing self-attention and cross-attention encoders to extract wave-frequency features. We introduce innovative pretext tasks, such as cross-domain wave peaks and frequency-energy predictions, to integrate essential knowledge about salient waveforms associated with sleep states, complemented by temporal contrastive learning to align wave-frequency features. Extensive experiments on ISRUC, SleepEDF-SC, and SleepEDF-ST datasets demonstrate the superior performance of Wafformer on the sleep staging task. Besides, Wafformer can adaptively capture dynamic waveforms related to sleep stages, offering a comprehensive understanding of sleep phenomena.

Existing non-example class-incremental learning (NECIL) methods usually utilize a combination strategy of replay mechanism and knowledge distillation. However, this combination strategy only focuses on the preservation of old information quantitatively, ignoring the preservation quality. When the old knowledge has wrong redundant information, catastrophic forgetting is more likely to occur. Therefore, obtaining adequate information without impurities as much as possible and removing invalid or even harmful information has become an effective solution to improve the performance of NECIL. This process is consistent with the information bottleneck (IB) theory. Thus, we propose a new NECIL method based on the IB framework. By using the different information obtained from the new and old class samples and the implicit knowledge in the teacher model training process, the error of harmful redundant information learned is eliminated. Specifically, we propose two optimization strategies that align with the two optimization processes of the information bottleneck. Firstly, we employ a pseudo-prototype selection mechanism that selectively incorporates pseudo-samples into the learning process of new and old categories, thus enhancing the distinction between new and old categories and diminishing the mutual information between the input and intermediate features. Secondly, we introduce an attention-based feature distillation method that regulates the distillation strength between feature pairs based on their similarity, thereby augmenting the mutual information between intermediate features and output prediction. Extensive experiments on three benchmarks demonstrate that the proposed method exhibits significant incremental performance improvements over existing methods.

We developed an intelligent shadow play system (ShadowTouch) based on multi-dimensional interactive perception technologies to let user interact with the shadow play figure naturally. ShadowTouch included a shadow play hardware subsystem and a multi-dimensional interactive software subsystem. In shadow play hardware subsystem, each joint of the shadow play figure was actuated by the connecting rod which was rotated through one steering gear. In multi-dimensional interactive software subsystem, a somatosensory interaction module synchronized motions between shadow play figure and human body based on skeleton point detection, and an automatic choreography module generated the motions automatically for the shadow play figure based on musical emotion recognition. The proposed system improved the immersive interaction experience and let users feel involved during the performance of shadow play, and the motions of shadow play figure (steering gear motion angles) could be saved digitally to further be applied for the reactivation and inheritance of traditional culture.