Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.

Background: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human–computer interaction, and brain–computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions. Methods: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability. Results: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods. Conclusions: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments.

Sleep disorders and understanding sleep mechanisms. However, traditional deep learning models often fail to effectivelycapture both temporal dependencies within Electroencephalogram(EEG) signals and the semantic correlations betweenmultimodal inputs. In this study, we propose a dual-stream Transformer-based framework that integrates raw EEG andelectro-oculogram (EOG) signals and their corresponding time-frequency (TF) representations through a cross-attentionfusion mechanism. Each modality is first processed by independent feature extractors, followed by pre-trained channellevel Transformers to capture intra-channel temporal dependencies. Finally, a global Transformer module is used for feature extraction, and the learned representations are classified using an optimized XGBoost classifier to enhancediscrimination ability. Experimental results on the Sleep-EDF-20, Sleep-EDF-78, and ISRUC-S3 datasets show thatthe proposed model achieves accuracies of 88.5%, 86.8%, and 84.0%, respectively, outperforming several state-of-the-artbaselines and confirming the effectiveness of the proposed multimodal fusion and hybrid Transformer-XGBoost designfor sleep stage classification.

Collaborative brain–computer interfaces (cBCIs) extend single-user BCIs to multi-brain recordings, enabling the investigation of inter-brain synchrony and cooperative neural processing. Motor imagery (MI) paradigms are of particular interest but face challenges such as low signal-to-noise ratio, inter-subject variability, and artifact contamination in electroencephalography (EEG) data. Independent component analysis (ICA), though widely applied in single-user MI-BCIs, is suboptimal in cBCIs since it is performed separately for each participant, thereby overlooking shared neural dynamics and potentially distorting inter-brain coupling. To address these limitations, this work proposes a joint ICA-based preprocessing framework that jointly decomposes EEG data from paired participants to enhance artifact suppression while preserving cross-brain synchrony. The performance of joint ICA is evaluated against subject-wise ICA from two perspectives: (i) motor imagery decoding accuracy, examined using spatial filtering with subsequent linear discriminant classification and a convolutional neural network architecture, and (ii) inter-brain synchrony quantified by the phase locking value (PLV) across homologous electrode pairs. Experimental results show that joint ICA improves decoding accuracy by 4.8% and 3.67% in the respective pipelines, while also yielding significantly stronger inter-brain PLV. These findings demonstrate that joint ICA offers an effective preprocessing strategy to improve both neural fidelity and classification robustness in MI-driven cBCI systems.