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Surface electromyogram (sEMG) is widely used in active rehabilitation control for stroke patients. However, the accuracy of movement recognition using sEMG signals is affected by abnormal states such as muscular fatigue and muscle weakness. In this article, a multimodal fusion strategy of electroencephalogram (EEG) and sEMG is proposed to improve the accuracy and robustness of hand motion recognition. It is an end-to-end approach based on a graph theory, in which the temporal signals of EEG and sEMG are considered as the features of nodes, and the functional connectivity is considered as the weights of edges. Four topologies, namely, 2EnMe, 2EwMe, 5EnMe, and 5EwMe, are proposed, and two standardization methods are tested for each topology. Then, three functional connectivity methods are investigated, namely, Pearson coefficient, mutual information, and coherence. Ten rounds of fivefold cross validation show that graph convolutional network (GCN)-2EnMe with the Pearson coefficient and min–max standardization is the best fusion model. At the fatigue levels of 0% and 30%, the achieved average accuracies are, respectively, 93.86% and 91.23%, which are significantly higher than those when using a parallel fusion method and a single-modality model. Moreover, the accuracy decrease ratio (ADR) of GCN-2EnMe is 2.80%, which is considerably better than that of a convolutional neural network (CNN) with parallel fusion (7.87%) and a CNN with single-modality sEMG (11.15%). The results show that the proposed novel EEG and sEMG fusion method has the potential to improve the accuracy and reliability of active control for stroke rehabilitation. [ABSTRACT FROM AUTHOR]

Here, four normalized difference water index (NDWI) variants, i.e., NDWI(860,970), NDWI(860,1240), NDWI(860,1640), and NDWI(1240,1640) are generated from the corn-oriented PROSAIL radiative transfer model. It is found that, instead of the linear relationship derived in previous studies, corn canopy water content (CWC) is best approximated as an exponential function of NDWI. Following the analysis of the PROSAIL-generated results, a newly optimized NDWI-based scheme is proposed for estimating corn CWC according to variations in the performance of the four NDWI variants under different CWC conditions. Validation results based on independent field data from the SMEX02, HiWATER2012, and Baoding2018 field experiments verify that this optimized NDWI-based corn CWC estimating scheme has a higher accuracy ($R = 0.87\,\,\pm \,\,0.03$ , RMSE = 0.2068 ± 0.0145 kg/m2) than existing NDWI-based strategies for corn CWC retrieval. The feasibility of retrieving corn vegetation water content (VWC) based on the optimized NDWI-based scheme is also investigated, and the superiority of the optimized NDWI-based scheme for retrieving corn VWC is assessed. By comparing with four other NDWI-based corn VWC estimating methods, as well as the corn VWC parameterization scheme applied in the SMAP soil moisture algorithm, it is shown that our optimized NDWI-based scheme has the best VWC estimation accuracy, with the highest $R$ of 0.89 ± 0.02 and the lowest RMSE of 0.7179 ± 0.0555 kg/m2. [ABSTRACT FROM AUTHOR]