Decoding Multi-Brain Motor Imagery From EEG Using Coupling Feature Extraction and Few-Shot Learning.

Electroencephalography (EEG)-based motor imagery (MI) is one of brain computer interface (BCI) paradigms, which aims to build a direct communication pathway between human brain and external devices by decoding the brain activities. In a traditional way, MI BCI replies on a single brain, which suffers from the limitations, such as low accuracy and weak stability. To alleviate these limitations, multi-brain BCI has emerged based on the integration of multiple individuals' intelligence. Nevertheless, the existing decoding methods mainly use linear averaging or feature integration learning from multi-brain EEG data, and do not effectively utilize coupling relationship features, resulting in undesired decoding accuracy. To overcome these challenges, we proposed an EEG-based multi-brain MI decoding method, which utilizes coupling feature extraction and few-shot learning to capture coupling relationship features among multi-brains with only limited EEG data. We performed an experiment to collect EEG data from multiple persons who engaged in the same task simultaneously and compared the methods on the collected data. The comparison results showed that our proposed method improved the performance by 14.23% compared to the single-brain mode in the 10-shot three-class decoding task. It demonstrated the effectiveness of the proposed method and usability of the method in the context of only small amount of EEG data available.

Oxymatrine blocks the NLRP3 inflammasome pathway, partly downregulating the inflammatory responses of M1 macrophages differentiated from THP-1 monocytes.

Many chronic inflammatory diseases, such as autoimmune inflammation, are associated with M1 macrophages, and the key to their treatment is blocking inflammation. Oxymatrine (OMT), a traditional Chinese medicine, has a marked anti-inflammatory effect. However, its anti-inflammatory target and mechanism in M1 cells remain unclear, which limits its clinical application. In this study, we investigated the anti-inflammatory effects of oxymatrine (OMT) on the M1 inflammatory response. We also determined the relationship between OMT treatment and the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) pathway with OMT treatment. To this end, we induced the differentiation of human peripheral blood monocytes (THP-1) into M1 cells. THP-1 cells were induced with a phorbol ester (phorbol-12-myristate-13-acetate (PMA)) and differentiated into naïve M0 macrophages. M0 cells were induced into M1 cells using lipopolysaccharide (LPS). The experimental groups were divided into the M0 macrophage group (NC), M1 inflammatory response group (LPS group), and M1 group treated with different concentrations of OMT (LPS + OMT-L, LPS + OMT-M, LPS + OMT-H). The cells in the OMT-treated groups were treated with OMT for 6 h, followed by LPS for 24 h, and the LPS group was treated with LPS only. The resulting supernatants and cells were collected. The secretion levels of NO were detected by the Griess method and the secretion levels of TNF-α and IL-1ß in the supernatants were detected by the ELISA method. The secretion levels of these inflammatory factors were reduced in every OMT-treated group compared to the LPS group (P < 0.01), and the most significant reductions were found in the OMT-H group (P < 0.0001). By western blotting, the protein expression levels of TLR4, NF-κB, NLRP3, and Caspase-1 were all found to be downregulated in the cells of OMT-treated groups compared to the LPS group (P < 0.0001). In situ changes in NLRP3 expression were observed using immunofluorescence. The fluorescence intensity of NLRP3 in M1 cells was weaker in all OMT intervention groups than in the LPS group (P < 0.001). In conclusion, OMT has significant anti-inflammatory effects on the M1 inflammatory responses, and the TLR4/NF-κB/NLRP3 pathway was blocked proportional to the concentration of OMT.