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.

Identifying motor imagery (MI) electroencephalogram (EEG) is an important way to achieve brain–computer interface (BCI), but its applicability is heavily dependent on the performance of feature extraction procedure. In this paper, a feature extraction method based on generalized maximum fuzzy membership difference entropy (GMFMDE) and discrete wavelet transform (DWT) was proposed for the feature extraction of EEG signals. The influence of different distance calculation methods, embedding dimensions and tolerances were studied to find the best configuration of GMFMDE for the feature extraction of MI–EEG. The gradient boosting decision tree (GBDT) classifier was used to classify the features extracted from GMFMDE and DWT. The average classification accuracy of 93.71% and the maximum classification accuracy of 96.96% were obtained, which proved the effectiveness of the proposed feature extraction method for EEG signal feature extraction.

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.

Obtaining accurate information on stratigraphic conditions and drilling status is necessary to ensure the safety of the drilling process and to guarantee the production of oil and gas. Sensing while drilling and intelligent monitoring technology, which employ multiple sensors and involve the use of intelligent algorithms, can be used to collect downhole information in situ to ensure safe, reliable, and efficient drilling and mining operations. These approaches are characterized by effective sensing and comprehensive utilization of drilling information through the integration of multi-sensor signals and intelligent algorithms, a core component of machine learning. The article summarizes the current research status of domestic and international sensing while drilling and intelligent monitoring technology using systematically collected relevant information. Specifically, first, the drilling-sensing methods used for in situ acquisition of downhole information, including fiber-optic sensing, electronic-nose sensing, drilling engineering-parameter sensing, drilling mud-parameter sensing, drilling acoustic logging, drilling electromagnetic wave logging, and drilling seismic logging, are described. Next, the basic composition and development direction of each sensing technology are analyzed. Subsequently, the application of intelligent monitoring technology based on machine learning in various aspects of drilling- and mining-status identification, including bit wear monitoring, stuck drill real-time monitoring, well surge real-time monitoring, and real-time monitoring of oil and gas output, is introduced. Finally, the potential applications of sensing while drilling and intelligent monitoring technology in deep-earth, deep-sea, and deep-space contexts are discussed, and the challenges, constraints, and development trends are summarized.

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.

In this research, we aimed to design a novel brain-computer interaction (BCI) approach in routing and forwarding mechanisms to realize a networking multi-modal and multi-brain linked control. In recent years, the field programmable gate array (FPGA) has become a popular choice to construct a heterogeneous network for routing and forwarding processes due to its ability to complete a high performance computation in single node. Conventional BCI mode focus on capturing the dynamic EEG signals from multiple electrode channels and this concept neglects the existing complexity and diversity of connecting information between two brain regions. To this moment, we build a heterogeneous multi-agent system (HMAS) architecture to simulate the memristive neuron networks (MNN) with a set of logic units for multi-agent in the Internet. Specifically, we first feed the non-linear features into an intelligent router to adjust their routing and forwarding processes. Subsequently, at one-time step, the output of all testing nodes is written into a heterogeneous logic space, which mainly consists of the sub-agent, forward port, and memory memristive neuron. In the FPGA, each programmable cell selectively integrates and stores a huge number of motion information between multiple interacting channels. We can achieve the networking cooperative functions of forward and route through FPGA large-scale computation. According to the extensive experimental results on several data, we validate the effectiveness of the proposed HMAS-BCI architecture to compare the optimization method of the multiple simulations.