With the rapid development in technology, computer aided detection or diagnosis has become an indispensable part of the medical industry. Automatic detection of epileptic events is one of the important subjects that have aroused wide interest from more and more investigators. This paper proposes a new model in classification of multi-category electroencephalogram (EEG) signals using time-frequency image and block texture features. The one-dimensional EEG is first mapped to time-frequency domain by means of short-time Fourier transform (STFT), which is adapted to obtain a two-dimensional time-frequency image (2D-TFI). With the idea of multi-scale blocking, the obtained phase images and amplitude images are divided into several sub-blocks corresponding to different frequency ranges and time periods. Then the texture features are calculated to describe the behaviour of EEG signals. Particularly, a novel quadratic feature selection method based on kernel entropy component analysis (KECA) and Kruskal-Wallis test (KW) has been proposed for dimension reduction, by which the features that contained most distinctive information were provided. Eventually, the optimal KECA-based features are fed to support vector machine (SVM) for deciding the class of corresponding EEG. The proposed model is found to achieve at least 99.30% accuracy, 98.0% sensitivity and 100% specificity for each of the eight clinical problems. Our scheme is proven to be effective for seizure detection, which can help doctors optimize the diagnosis workflow, reduce workload and improve detection precision.

Metaverse is an initiative, an immersive virtual-reality (VR) digital space that installs to the next-generation Internet for a platform of network human–computer interaction mode. As the access point number of routing/switching process in a nonlinear heterogeneous scheme (NLHS) is increased, the original method for the word, voice, or image transmission cannot conform to the node and link delay needs of multilead electroencephalogram, and multimedia audio/video operating in brain–computer interface or VR terminal. The parallelization and distribution of data streams with routing/switching capability were not only embed in software-defined network but also play a critical role in software-defined radio. The purpose of this manuscript is to enable the Metaverse to get an initiative control and immersive perceptual abilities, we introduce an NLHS to make a novel initiative, immersive human–computer interactions: software-defined memristive neural networks (MNNs). In the link/network layer, we use the function of software-defined approach in control of a threshold during the early high-speed switching fabric in EEG input or audio/video output. However, the multiple control or perceptual data streams of our thought and sense in conventional routing nodes remain a poorly serial running state all the time. Here, we design a large-scale switch parallelization of memristor-based FPGA system on a basis of previous NLHS for the designing of a routing node in MNN. To conclude, our results verify a high performance in the novel human–computer interaction with multilead/multimedia information.

Early detection and diagnosis are critical for effective depression management. Although electroence-phalography (EEG) can provide an objective basis for the auxiliary diagnosis of depression, decoding depression-related brain activity from EEG is a highly challenging task due to the inherent complexity, dynamism, and non-linearity. Therefore, this study introduces a novel temporal dual-stream feature extraction network (TDSFE-Net) that incorporates multiple attention mechanisms. Specially, we first develop a dynamic fusion weight based local-global attention mechanism into the hierarchical temporal-separable convolutional network (TSCN) to automatically capture the temporal dynamic characteristics of the EEG signal. Subsequently, a channel-wise module is designed to reveal the key temporal information in spatial dimensions. Finally, a softmax with full conected layer is used as classifier.The TDSFE-Net achieved impressive classification accuracies of 98.72%, 96.91%, and 99.53% on the MODMA, HUSM, and Hospital datasets, respectively. In addition, this study also reveals the pattern of correlation between the activity of specific brain regions and depression, providing a new perspective and scientific basis for discovering biomarkers and studying the neural mechanisms of depression.