Electroencephalogram (EEG) signals provide rich spatiotemporal brain features crucial for epilepsy diagnosis. Though traditional deep neural networks combining attention mechanisms excel in feature extraction, these models do not fully emulate the neural processing efficiency of the brain and they fall short in fully harnessing the spatiotemporal dynamics of EEG data. This study introduces the dual attention-driven and feature fusion-based spiking neural network (DAFF-SNN), which synergizes the spatiotemporal attention mechanism with SNNs' inherent temporal processing prowess to enhance the precision and efficiency for epilepsy detection. The DAFF-SNN utilizes an adaptive spiking fusion module (ASFM) for feature integration, optimizing the feature fusion process by exploiting the spatiotemporal complementarity of EEG data through a spike-driven strategy. Evaluations on the Bonn, Beriut, CHB-MIT, and Siena datasets demonstrate DAFF-SNN's high accuracies (100%, 97.6%, 98.2%, 99.6% ), achieving comparable or superior performance to state-of-the-art ANN methods, highlighting its efficient epilepsy detection capability.

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 hierarchiclal 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.

Metaverse is an initiative, immersive virtual-reality 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 non-linear heterogeneous scheme is increased, the original method for the word, voice, or image transmission can not conform to the node and link delay needs of multi-lead electroencephalogram, and multi-media audio/video operating in brain-computer interface or virtual-reality terminal. The parallelization and distribution of data streams with routing/switching capability was not only embed in software-defined network but also plays 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 non-linear heterogeneous scheme to make a novel initiative, immersive human-computer interactions: Software-defined memristive neural networks. 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 non-linear heterogeneous scheme for the designing of a routing node in memristive neural network. To conclude, our results verify a high performance in the novel human-computer interaction with multi-lead/multi-media information.

In this research, we aimed to design a architecture of brain-computer interaction (BCI) in routing nodes routing and forwarding mechanism by exploring the memristive neuron networks (MNN) among heterogeneous multi-agent system (HMAS). In recent years, field programmable gate array (FPGA) has become a popular choice to construct a heterogeneous network system for routing and forwarding mechanism due to its ability to describe a hardware information system in a software defined method. However, most BCI approaches focus only on capturing the dynamics of EEG signal by simply receiving all wave of individual or making up them as a single system. Such methods neglect the existing problems of traditional BCI mode. To this moment, we propose a novel Heterogeneous Multi-Agent System (HMAS) to model the MNN among a group of logic units for realizing the multi-agent system in BCI approach. Specifically, we first feed each agent's non-linear feature into a routing node to model the routing and forwarding functions. Subsequently, at one time step, the outputs of all testing routers are fed into a heterogeneous network space, which mainly consists of multiple sub-agents, a new forward port, and some memory memristive neuron which build a non-linear network system to set up a multiple agent in MNN environment. In the FPGA logic unit, each sub-memory unit stores individual motion information, while these programmable units selectively integrates and stores a huge number of motion information between multiple interacting persons from multiple sub-memory units via the forwarding ports and MNN storage unit, respectively. According to the extensive experimental results on several experimental datasets validate the effectiveness of the proposed HMAS-BCI by comparing against a optimization method of the experimental scene.