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In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimensional (1D) features of EEG signals into many segments and employ 1D-CNNs. Moreover, these investigations are further constrained by the absence of consideration for temporal links between time series segments or spectrogram images. Therefore, we propose a Dual-Modal Information Bottleneck (Dual-modal IB) network for EEG seizure detection. The network extracts EEG features from both time series and spectrogram dimensions, allowing information from different modalities to pass through the Dual-modal IB, requiring the model to gather and condense the most pertinent information in each modality and only share what is necessary. Specifically, we make full use of the information shared between the two modality representations to obtain key information for seizure detection and to remove irrelevant feature between the two modalities. In addition, to explore the intrinsic temporal dependencies, we further introduce a bidirectional long-short-term memory (BiLSTM) for Dual-modal IB model, which is used to model the temporal relationships between the information after each modality is extracted by convolutional neural network (CNN). For CHB-MIT dataset, the proposed framework can achieve an average segment-based sensitivity of 97.42%, specificity of 99.32%, accuracy of 98.29%, and an average event-based sensitivity of 96.02%, false detection rate (FDR) of 0.70/h. We release our code at https://github.com/LLLL1021/Dual-modal-IB. [ABSTRACT FROM AUTHOR]

Feature selection plays a vital role in the detection and discrimination of epileptic seizures in electroencephalogram (EEG) signals. The state-of-the-art EEG classification techniques commonly entail the extraction of the multiple features that would be fed into classifiers. For some techniques, the feature selection strategies have been used to reduce the dimensionality of the entire feature space. However, most of these approaches focus on the performance of classifiers while neglecting the association between the feature and the EEG activity itself. To enhance the inner relationship between the feature subset and the epileptic EEG task with a promising classification accuracy, we propose a machine learning-based pipeline using a novel feature selection algorithm built upon a knockoff filter. First, a number of temporal, spectral, and spatial features are extracted from the raw EEG signals. Second, the proposed feature selection algorithm is exploited to obtain the optimal subgroup of features. Afterwards, three classifiers including k -nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) are used. The experimental results on the Bonn dataset demonstrate that the proposed approach outperforms the state-of-the-art techniques, with accuracy as high as 99.93% for normal and interictal EEG discrimination and 98.95% for interictal and ictal EEG classification. Meanwhile, it has achieved satisfactory sensitivity (95.67% in average), specificity (98.83% in average), and accuracy (98.89% in average) over the Freiburg dataset. [ABSTRACT FROM AUTHOR]

In this study, Heusler alloy has been prepared by solid state hybrid microwave heating within 40 min. X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) analyses reveal that the obtained samples are of single phase cubic structure and homogeneous. Temperature-dependence of electrical resistivity and magnetization measurements confirm the austenite to martensitic transition of the first-order nature with the absence of the exchange bias (EB), which is verified by the magnetic hysteresis loops. Compared with the samples prepared by conventional arc-melting method, the sample synthesized by fast microwave heating has lower martensitic transition temperature, smooth magnetization change in the martensitic region and disappeared EB, implying strongly suppressed antiferromagnetic (AFM) interaction. These results suggest that tuning the microstructure via different synthesis method can influence significantly the martensitic transition and magnetic interactions in the Ni-Mn-Sb system. [ABSTRACT FROM AUTHOR]

In this study, single phase cobalt disulfide was synthesized by temperature-controlled solid state hybrid microwave heating. The structure, composition and morphology of the obtained samples were studied using X-ray diffraction (XRD), scanning electron spectroscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and high resolution transmission electron microscopy (HRTEM), respectively. The loose polycrystalline precursor was then hot pressed to dense bulk sample. The subsequent transport and magnetic properties measurements reveal the ferromagnetic Curie temperature at the magnetic transition near 128 K. These results suggest that the magnetic transition in is susceptible to the preparation conditions and the microstructure of the samples. [ABSTRACT FROM AUTHOR]

In this paper, we report an efficient preparation of silver telluride by rapid solid-state microwave heating. Single phase products have been obtained within as little as 30 min of irradiation time. The rate of the synthesis is significantly increased as compared to the solid state reaction method in a conventional furnace. The quality of the as-prepared samples are attested by structural, compositional and magnetoresistance characterizations. [ABSTRACT FROM AUTHOR]