Toward the development of effective and efficient brain–computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson’s matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.

Music can evoke a variety of emotions, which may be manifested by distinct signals on the electroencephalogram (EEG). Many previous studies have examined the associations between specific aspects of music, including the subjective emotions aroused, and EEG signal features. However, no study has comprehensively examined music-related EEG features and selected those with the strongest potential for discriminating emotions. So, this paper conducted a series of experiments to identify the most influential EEG features induced by music evoking different emotions (calm, joy, sad, and angry). We extracted 27-dimensional features from each of 12 electrode positions then used correlation-based feature selection method to identify the feature set most strongly related to the original features but with lowest redundancy. Several classifiers, including Support Vector Machine (SVM), C4.5, LDA, and BPNN, were then used to test the recognition accuracy of the original and selected feature sets. Finally, results are analyzed in detail and the relationships between selected feature set and human emotions are shown clearly. Through the classification results of 10 random examinations, it could be concluded that the selected feature sets of Pz are more effective than other features when using as the key feature set to classify human emotion statues.

Brain-computer interface (BCI) based on motor imagery (MI) can help patients with limb movement disorders in their normal life. In order to develop an efficient BCI system, it is necessary to decode high-accuracy motion intention by electroencephalogram (EEG) with low signal-to-noise ratio. In this article, a MI classification approach is proposed, combining the difference in EEG signals between the left and right hemispheric electrodes with a dual convolutional neural network (dual-CNN), which effectively improved the decoding performance of BCI. The positive and inverse problems of EEG were solved by the boundary element method (BEM) and weighted minimum norm estimation (WMNE), and then the scalp signals were mapped to the cortex layer. We created nine pairs of new electrodes on the cortex as the region of interest. The time series of the nine electrodes on the left and right hemispheric are respectively used as the input of the dual-CNN model to classify four MI tasks. The results show that this method has good results in both group-level subjects and individual subjects. On the Physionet database, the averaged accuracy on group-level can reach 96.36%, while the accuracies of four MI tasks reach 98.54, 95.02, 93.66, and 96.19%, respectively. As for the individual subject, the highest accuracy is 98.88%, and its four MI accuracies are 99.62, 99.68, 98.47, and 97.73%, respectively.

High-impedance faults in three-core power cables gradually develop into serious faults. Hence, localizing high-impedance faults is the key to ensuring transmission line reliability. The fault location cannot be accurately located due to the low detection sensitivity of the time-domain reflection (TDR) method and interference peaks in the localization results of the frequency-domain reflection (FDR) method. A new spectrum of propagation function (SPF) based fault localization method is proposed in this paper. Cable's S-parameter model is first described to reveal the reason for the change in the resonant frequency in the SPF due to high-impedance faults. In addition, the integral transform algorithm is employed to extract the cable's fault location from the SPF; the method's feasibility is numerically verified. Finally, high-impedance faults are set up in a three-core cable of 100 m in length, and the SPF of the cable is measured via a vector network analyzer. Compared with the TDR and FDR methods, the proposed approach is more capable of identifying high-impedance faults and more dependable on localization results. The fault localization error is within 0.16%, which has good application value.

Multiple potential defects in cables cannot be detected and localized due to the low detection sensitivity of the time domain reflectometry method, as well as the cross-terms interference problem of the time and frequency domain reflectometry method. A new method for fault detection in underground power cables is proposed based on the spectrum of propagation function. First, the propagation characteristics of various frequency signals in cables are analyzed to identify the causes of local irregularities in the spectrum of the propagation function resulting from defects. Furthermore, the locations of defects are successfully identified using the integral transform algorithm. As broadband impedance spectroscopy cannot accurately measure the input impedance of a 163 m cable for defect detection, the spectrum of propagation function is more suitable for long-distance cables. Finally, cables of 50 m, 66 m, and 163 m lengths are examined for mechanical damage and thermal aging defects. The fault localization error is within 0.21%, providing a technical reference for fault detection of underground cables.