WiMi Hologram Cloud Inc (WIMI)

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WIMI (NASDAQ:WIMI) develops a humanoid control system based on mixed-signal BCI brain-computer interface technology.

Source
https://t.cj.sina.com.cn/articles/view/1747383115/6826f34b020018m40?from=tech

May 4, 2023

In recent years, hybrid BCI technology has gradually been widely used in the field of brain-computer interface, which combines multiple complementary signal sources, such as electromyography (EMG), electroencephalogram (EEG), electrooculogram (EOG) and steady-state Visual evoked potential (SSVEP), etc., through data fusion technology to improve accuracy and robustness. Hybrid BCI technology has become an important technical framework because it can record and analyze multiple complementary signals, and use data fusion technology combined with machine learning algorithms to fuse these signals. It is reported that WIMI has made significant progress in the field of hybrid BCI technology, and has developed a humanoid control system based on the hybrid BCI brain-computer interface.

According to the data, the technical realization path of the humanoid control system of WIMI (NASDAQ: WIMI) hybrid BCI brain-computer interface includes multiple steps.
- First, multiple sensors need to be used to record multiple complementary signals such as electromyography (EMG), electroencephalogram (EEG), electrooculogram (EOG) and event-related desynchronization (ERD), steady-state visual evoked potential ( SSVEP) and near infrared spectroscopy (NIRS). The signals recorded by these sensors need to be pre-processed to remove interfering signals, noise reduction, etc.
- Then, machine learning algorithms are used to perform feature extraction, signal classification and other operations on the signal, so as to realize accurate decoding of the brain-computer interface signal.
- Finally, map the decoded result to the humanoid robot control to realize the control of the humanoid robot.

Compared with traditional BCI technology, the humanoid control system of WIMI holographic hybrid BCI brain-computer interface has many advantages.
- First, by recording and analyzing multiple complementary signals, the activity information of the brain can be obtained more comprehensively, thus improving the accuracy and robustness of decoding.
- Secondly, data fusion technology can further improve the robustness and reliability of the system and avoid recognition errors caused by the specificity of a single signal. In addition, the application of machine learning algorithms can further improve the decoding speed and accuracy, thereby increasing the information transmission rate.
- Finally, the humanoid control system based on hybrid BCI technology can achieve more natural and precise control, and can be applied to many fields such as robot assistance and assistance for the disabled.

Compared with traditional BCI technology, the specific advantages of hybrid BCI technology are as follows:
Improved accuracy and robustness:
Hybrid BCI technology utilizes multiple complementary signal sources such as electromyography (EMG), electroencephalogram (EEG), electrooculogram (EOG) and steady-state visual evoked potential (SSVEP) ) event-related desynchronization, etc., through data fusion techniques to improve accuracy and robustness. Multiple signal sources can provide more comprehensive and reliable information than a single source, increasing the accuracy and robustness of the system.

Enhanced information transmission rate:
In traditional BCI technology, a single signal source may not be able to provide enough information to achieve high-speed human-computer interaction. Hybrid BCI technology, on the other hand, combines multiple signal sources to enhance the rate of information transmission, enabling faster and more natural human-computer interaction.

Improved applicability and operability:
Hybrid BCI technology takes advantage of multiple signal sources, which can improve system applicability and operability. For example, some users may not be able to interact effectively through a single signal source, but the combination of multiple signal sources can provide more choices and make it easier to achieve effective interaction.

Improved training efficiency:
In traditional BCI technology, the training of a single signal source usually requires a lot of time and effort. The hybrid BCI technology can take advantage of multiple signal sources, improve training efficiency through data fusion technology, and achieve reliable interaction faster.

The technical framework of hybrid BCI is mainly based on technologies such as signal acquisition, signal preprocessing, feature extraction, feature selection, and classifier training. Higher control precision and robustness can be achieved through the combined use of multiple signal sources and machine learning algorithms. WIMI's hybrid BCI humanoid control system uses a variety of signal sources, including electromyography (EMG), electroencephalogram (EEG), electrooculogram (EOG) and near-infrared spectrum, etc., and integrates these signals through data fusion technology Sources are combined to improve the accuracy and robustness of the control system on the one hand. At the same time, the system also has high-speed information transmission capabilities, enabling users to realize natural and efficient human-computer interaction through simple thought commands.

The technical framework and specific implementation path of WIMI (NASDAQ:WIMI) hybrid BCI can be divided into the following steps:
Signal Acquisition:
Use multiple sensors to acquire multiple complementary signal sources, such as electromyography (EMG), electroencephalogram (EEG), electrooculogram (EOG), event-related desynchronization (ERD), steady-state visual evoked potentials (SSVEP) and near-infrared spectroscopy (NIRS), etc. These signal sources can provide different information, such as muscle movement, brain activity, attention, etc.

Signal preprocessing:
Preprocessing the collected signal, such as denoising, filtering, feature extraction, etc., to improve the quality and accuracy of the signal. For example, commonly used preprocessing methods such as average removal, bandpass filtering, wavelet transform, etc. can be used to reduce signal noise and extract useful features.

Feature extraction:
Use machine learning algorithms to extract features from the preprocessed signal, such as time domain features, frequency domain features, wavelet transform, etc. These features can provide important information about brain or muscle movement.

Feature selection:
Feature selection is performed according to the importance of features to reduce the number of features and computational complexity. For example, regularization-based sparsification can be used to select important features.

Classifier training:
use the training set to train the classifier, such as support vector machine (SVM), random forest (Random Forest), etc. Classifiers can map input signals to specified actions or commands.

System integration:
Integrate all components into a complete system, including signal acquisition, preprocessing, feature extraction, feature selection, and classifier training. The system can communicate with external devices, such as robots, prosthetics, or game controllers, and send commands or actions to the device.

WIMI's (NASDAQ: WIMI) hybrid BCI brain-computer interface humanoid control system also has good applicability and operability, and can adapt to the needs and characteristics of different users. The training efficiency of the system is also high, and users can complete the training in a short time and quickly achieve reliable interactive effects.

WIMI holographic hybrid BCI brain-computer interface humanoid control system has broad application prospects. For example, it can be applied to the rehabilitation and auxiliary treatment of the disabled. By monitoring and identifying the muscle and brain signals of the disabled, it can realize the precise control of humanoid robots, thereby helping the disabled to live and work more autonomously. In addition, this technology can also be widely used in the field of production and manufacturing. Through the monitoring and identification of employees' muscle and brain signals, precise control of production line robots can be achieved, thereby improving production efficiency and product quality. Apply it to fields such as medical care, smart home and entertainment to bring people a more convenient and efficient life experience. In addition, WIMI will continue to promote technological innovation and research and development, constantly improve product performance and functions, and provide users with better services and experiences. With the continuous development and application of hybrid BCI technology, WIMI holographic hybrid BCI brain-computer interface humanoid control system will bring users a more intelligent and efficient human-computer interaction experience, and make more contributions to the development of brain-computer interface technology.