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.

Transfer learning is the method that makes use of knowledge from other fields to solve problems in related fields. It has been shown that it can deal with the problem of insufficient labeled data for new users or new tasks in the brain-computer interface. Domain adaptation is one of the transfer learning methods which is widely used for its excellent performance. Here, the offline cross-subject EEG signal classification is mainly focused on. The unlabeled EEG trials of the new user are classified by using the EEG trials with labels from source subjects. In this paper, a novel transfer learning method called multi-source fusion adaptation regularization (MFAR) is proposed. MFAR preprocesses the EEG signal by aligning the motor imagery trials to their resting state trials, and can reduce the differences among subjects. It also defines a learning framework by combining weighted balanced distribution adaptation (W-BDA), source empirical risk, and manifold regularization to further reduce the variation between source and target domains. We validated the method on two BCI Competition IV datasets for motor imagery tasks. In the absence of labeled EEG trials of the target subject, compared with the excellent counterparts, the classification accuracy increases by 9.28% and 11.73%. After the alignment algorithm is added, the accuracy of the MFAR is improved by 9.36% and 4.17% on the basis. The experimental results show that our learning framework outperformed several state-of-the-art transfer learning algorithms. Even when the training data from the new user are sufficient, the proposed approach achieves good performance.

With the rapid development of brain-computer interface (BCI) technology, the effective integration of multimodal biological signals to improve classification accuracy has become a research hotspot. However, existing methods often fail to fully exploit cross-modality correlations in complex cognitive tasks. To address this, this paper proposes a Multi-Branch Convolutional Neural Network with Attention (MBC-ATT) for BCI based cognitive tasks classification. MBC-ATT employs independent branch structures to process electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals separately, thereby leveraging the advantages of each modality. To further enhance the fusion of multimodal features, we introduce a cross-modal attention mechanism to discriminate features, strengthening the model's ability to focus on relevant signals and thereby improving classification accuracy. We conducted experiments on the n-back and WG datasets. The results demonstrate that the proposed model outperforms conventional approaches in classification performance, further validating the effectiveness of MBC-ATT in brain-computer interfaces. This study not only provides novel insights for multimodal BCI systems but also holds great potential for various applications.

In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.