Multilayer network-based channel selection for motor imagery brain-computer interface.

Objective. The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach. We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine classifier with a linear kernel is trained to accurately identify MI tasks.Main results. We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (pairedt-tests,p< 0.05).Significance. The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.

Self-assembled CeVO4/Au heterojunction nanocrystals for photothermal/photoacoustic bimodal imaging-guided phototherapy.

Phototherapy, including photothermal therapy (PTT) and photodynamic therapy (PDT), has attracted great attention because it can effectively inhibit the proliferation and propagation of cancer cells. Recently, heterojunction nanomaterials have shown tremendous application value in the field of biological medicine. In this work, the CeVO4/Au heterojunction nanocrystals (NCs) are designed for photothermal/photoacoustic bimodal imaging-guided phototherapy. The as-synthesized hydrophobic oleic acid (OA)-stabilized CeVO4 nanosheets were modified with HS-PEG-OH for translating into hydrophilic ones, which can significantly improve their stability and biocompatibility. Subsequently, the plasmonic Au nanoparticles were in situ successfully deposited on the surface of HS-PEG-coated CeVO4 to form CeVO4/Au heterojunction NCs for improving the visible and near-infrared light absorption, which results in enhanced photothermal conversion performance and reactive oxygen species (ROS) generation capacity. Thus, the CeVO4/Au can cause more severe damage to cancer cells than pure CeVO4 under NIR laser irradiation. Also, CeVO4/Au can provide distinct tumor contrast by photothermal/photoacoustic bimodal bioimaging. Our results demonstrate that CeVO4/Au NCs could be used as an effective theranostic anticancer agent for near-infrared (NIR) light-mediated PTT and PDT.