WiMi Hologram Cloud is Developing A Memristor-based Neural Signal Analysis System for Efficient BCI
The system employs a new computational paradigm closer to how the human brain works, i.e., a memristor-based neural network system. The memristor-based neural network is characterized by high parallelism and low energy consumption. When processing neural signals, the system uses an end-to-end data processing process. That is, the acquired raw signal is directly converted into the final control signal through pre-processing, feature extraction, and classification recognition steps, thus avoiding the frequent data transmission and computation process in traditional architectures and significantly improving the efficiency and accuracy of the system. The memristor arrays can quickly process a large amount of data because they can hold information between each neuron, thus enabling highly parallel processing. This approach is similar to how neurons in the human brain communicate, allowing for more efficient data processing. In addition, memristor-based systems also have higher energy efficiency in storing and reading data, which can significantly reduce power consumption.
Compared with the traditional von Neumann architecture, the system improves brain-computer interface signal processing and linking capabilities hundreds of times. In addition, the system employs innovative hardware architecture and algorithm optimization to efficiently process neural signals from the human brain and convert them into computer-recognizable signals for seamless human-computer connectivity.
The path and manner of implementation of this technology are as follows. First, an array of memristors needs to be designed and prepared. A memristor is an electronic device that can change its resistance value in response to a voltage and remember previous voltage and current states. A memristor array is a circuit system consisting of many memristors that mimic the synaptic connections between neurons and record the postsynaptic potentials between neurons. The human brain's neural signals then need to be captured. Brain-computer interface technology usually uses electroencephalography (EEG), magnetic resonance imaging (MRI), and other methods to acquire neural signals. These signals are generally weak and require processing, such as signal amplification and filtering, to enhance the strength and accuracy of the signal. After the signal acquisition, data pre-processing is required, including noise removal, filtering, and feature extraction. These steps can improve the quality and accuracy of the signal and reduce misclassification and interference. The pre-processed neural signals are fed into the memristor array for simulation. In the memristor array, each memristor represents a neuron, and the connection and synaptic strength between them can be regulated employing voltage and current, etc. The memristor array can simulate and record the postsynaptic potentials and signaling between neurons. Finally, the simulation results of neural signals are interpreted and controlled by algorithms and other means. The brain-computer interface system can control external devices such as computers and prostheses by interpreting neural signals such as brain waves and can also realize applications such as human-computer interaction. In the practical application of this system, many details still need to be considered, such as different functional requirements, differences in the source of signal letter acquisition and the environment used, and other detailed adjustments to the application of different scenarios.
WiMi's system adopts the latest technological solution to achieve efficient neural signal processing and analysis by simulating the synaptic connections between neurons through an array of memristors. This technology will lead a new revolution in brain-computer interface technology and bring a more convenient and efficient intelligent interaction experience for human beings. The system also adopts adaptive adjustment algorithms and reinforcement learning algorithms, which can quickly adjust the parameters of the neural network according to the user's operating habits and intentions, thus achieving more accurate control. In addition, the system introduces multimodal sensors and multi-source data fusion technology, which can fuse data from different sensors to improve the accuracy and robustness of the signal.
Artificial intelligence, machine learning, and brain-computer interface technologies are developing rapidly. As an emerging hardware gas pedal with low power consumption, high speed, and high accuracy, memristor arrays have a broad application prospect in neural signal analysis. The neural signal analysis system based on memristor arrays has been verified in the laboratory for several experiments and achieved excellent results. Compared with traditional brain-computer interface technology, WiMi's memristor-based neural signal analysis system has higher efficiency and accuracy and can realize more complex control tasks and interaction modes. The launch of this system will significantly impact healthcare, education, and entertainment, bringing a smarter, more convenient, and more comfortable future for human beings.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
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