The brain activity pattern can be presented by Electroencephalogram (EEG), which is considered as an alternative to traditional biometrics. Researchers have done conducted studies on EEG-based identification, while few of them discussed the effect of time robustness which is very important for the identification system. In this study, we compared and analyzed the two runs EEG signals of restingstate of eye open/closed (REO/REC). The time intervals between two runs were at least two weeks. Here are 17 participants joined in this study. Each of them took two runs experiment. Each run contains four sessions, each session includes 150 seconds of REO/REC. Spectral and statistical analyses were used to extract feature. Three classifiers, Euclidean distance, SVM, and LDA, were used to get classification accuracies and to compare the performance between features of each run and two runs. The results of two runs PSD values of both REO and REC conditions show that there is a similarity within each subject and a difference between subjects. The classification accuracies of three methods of each run are almost 99%. The classification accuracies using two runs data as training set can also reach up to 97% while using each of two-run data as training set is nearly 80%. Thus, the features of most subjects have cross-time robustness and could be used as identification. This study will have an important role in EEG-based identification system.INDEX TERMS Electroencephalography(EEG), identification, resting-state, robustness.
The reaction process of polycarbosilane (PCS) fiber cured by cyclohexene vapor has been studied and compared with that of PCS fiber cured by air. The influence of curing temperature on SiÀ ÀH bond reaction degree and gel content, the structure and composition of PCS were investigated by FTIR, EA, TGA, NMR, and GC-MS. The results showed that, SiÀ ÀH bond in the molecular structure of PCS reacted during cyclohexene curing process and the reaction degree increased when the curing temperature increases. Simultaneously, gel content of PCS fiber rapidly increased till PCS fiber became infusible. SiÀ ÀH radical and SiÀ ÀCH 2 radicals formed SiÀ ÀCH 2 À ÀSi crosslinking of PCS molecules through the agency of cyclohexene. Some cyclohexyls linked to principal chain of PCS, which was proven by 13 C-CPMAS-NMR, and broke off, and cyclohexane and some monosilane are generated as byproducts when temperature increased.
The rupture of aneurysms is the main cause of spontaneous subarachnoid hemorrhage (SAH), which is a serious life-threatening disease with high mortality and permanent disability rates. Therefore, it is highly desirable to evaluate the rupture risk of aneurysms. In this study, we proposed a novel semiautomatic prediction model for the rupture risk estimation of aneurysms based on the CADA dataset, including 108 datasets with 125 annotated aneurysms. The model consisted of multidimensional feature fusion, feature selection, and the construction of classification methods. For the multidimensional feature fusion, we extracted four kinds of features and combined them into the feature set, including morphological features, radiomics features, clinical features, and deep learning features. Specifically, we applied the feature extractor 3D EfficientNet-B0 to extract and analyze the classification capabilities of three different deep learning features, namely, no-sigmoid features, sigmoid features, and binarization features. In the experiment, we constructed five distinct classification models, among which the k-nearest neighbor classifier showed the best performance for aneurysm rupture risk estimation, reaching an F2-score of 0.789. Our results suggest that the full use of multidimensional feature fusion can improve the performance of aneurysm rupture risk assessment. Compared with other methods, our method achieves the state-of-the-art performance for aneurysm rupture risk assessment methods based on CADA 2020.
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