By verifying the electromagnetic response characteristics of graphene in the low terahertz (THz) band, a terahertz metamaterial sensor is proposed. The unit cell of the metamaterial sensor is a split ring resonator nested square ring resonator. The split ring resonator with four gaps is made of lossy metal, and the square ring resonator is formed by graphene. This structure can produce two high-performance resonant valleys in the transmission spectrum of 0.1–1.9 THz. The quantum interference between metal–graphene hybrid units also produces a reverse electromagnetically induced transparency (EIT)-like resonant peak between the two resonant valleys. Compared with the bimetallic ring resonator having the same shape and size, the sensor can dynamically adjust the position of the lower frequency resonant valley, thus, realizing the active tuning of the bandwidth and amplitude of the EIT-like resonant peak. The results demonstrate that the proposed sensor has a better sensing performance and can improve the detection precision by tuning itself to avoid the interference of environmental factors and the properties of samples. Combined with the advantages of convenience, rapidity, and non-damage of terahertz spectrum detection, the sensor has a good application potential to improve the unlabeled trace matter detection.

Feature extraction and selection are important parts of motor imagery electroencephalogram (EEG) decoding and have always been the focus and difficulty of brain-computer interface (BCI) system research. In order to improve the accuracy of EEG decoding and reduce model training time, new feature extraction and selection methods are proposed in this paper. First, a new spatial-frequency feature extraction method is proposed. The original EEG signal is preprocessed, and then the common spatial pattern (CSP) is used for spatial filtering and dimensionality reduction. Finally, the filter bank method is used to decompose the spatially filtered signals into multiple frequency subbands, and the logarithmic band power feature of each frequency subband is extracted. Second, to select the subject-specific spatial-frequency features, a hybrid feature selection method based on the Fisher score and support vector machine (SVM) is proposed. The Fisher score of each feature is calculated, then a series of threshold parameters are set to generate different feature subsets, and finally, SVM and cross-validation are used to select the optimal feature subset. The effectiveness of the proposed method is validated using two sets of publicly available BCI competition data and a set of self-collected data. The total average accuracy of the three data sets achieved by the proposed method is 82.39%, which is 2.99% higher than the CSP method. The experimental results show that the proposed method has a better classification effect than the existing methods, and at the same time, feature extraction and feature selection time also have greater advantages.

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