Electroencephalogram (EEG)-based emotion recognition holds great potential in intelligent human-computer interaction and brain-computer interface systems, as the brain generates distinct electrical activity patterns under different emotional states. However, EEG information often contains data from numerous channels, leading to high computational cost and potential redundancy. Existing channel selection methods often rely on uniform rules, lacking frequency-specific adaptability and inter-channel modeling, which can cause information loss and reduced performance during dimensionality reduction. To address this issue, we propose a novel framework that combines discriminative channel selection with hierarchical spatial-temporal modeling to enhance both performance and efficiency. In preprocessing, wavelet coherence and mutual information are used to adaptively select informative channels across multiple frequency bands. The selected signals are then processed by a Spatial-Temporal Graph-aware Network (STG-Net), which models spatial relationships between channels through graph convolution, extracting spatial features from each time frame. Coupled with a temporal modeling module, the network further captures the evolving temporal patterns of emotional states across consecutive frames. Finally, frequency-spatial-temporal features are fused for emotion classification. Compared to the state-of-the-art methods, our approach achieves superior performance in both recognition accuracy and model efficiency.

This study proposes the concept of an adjacent two-end or multi-end link of a software-defined network to support the transmission of data from electroencephalogram as well as audio and video streaming through spread spectrum communications. Instead of solving the problem of the software-defined scheme of prioritization in networking-related calculation and communication, we identify the southbound–northbound structure of current neural or informational networks in control and perceptual interactions. The proposed framework of multi-lead and multi-media structures allows the human-centric metaverse to execute different operations for local and global planning schemes based on the transmission of high-speed lead or media-related data through spread spectrum streaming. This depends on the kind of brain–computer interaction and is similar to the system of multi-modal perception in routing or switching. First, we design the field-programmable gate array for conventional routing nodes used in computation by different neural networks. Second, we demonstrate its enhanced applicability with a software core for multiple routers in the computational power network of the Internet of Brains in different brain–computer smart terminals. The results of simulations verified the benefits of the proposed model and structure and revealed a variety of important operational indicators.

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.