Shulou(Shulou.com)11/24 Report--

Brain-computer interface (BCI) technology is a technology that directly connects the brain with external devices, which can realize the direct communication between the brain and the machine. This technology has great potential to reshape the interaction between human and machine, especially in medical and health, rehabilitation medicine, virtual reality, game entertainment and other fields.

In recent years, with the deepening of neuroscience research and the rapid development of big data and AI technology, BCI technology is also making continuous progress. Among them, the BCI technology based on EEG-fNIRS multimodal data fusion has received widespread attention recently. EEG (computer electroencephalogram) and fNIRS (functional near infrared spectroscopy) are two commonly used non-invasive brain imaging techniques. Due to the lack of time resolution and feature extraction technology, the existing BCI system based on fNIRS has poor performance. The development of BCI technology based on EEG-fNIRS multimodal data fusion still faces some challenges, such as data preprocessing, feature extraction and fusion, model training and optimization, which need to be solved by combining neuroscience knowledge with machine learning, deep learning and other AI technologies. Therefore, this is a highly intersecting research field, involving neuroscience, computer science, electronic engineering, biomedical engineering and other disciplines. It is reported that NASDAQ:WIMI has been devoting itself to the research and application of artificial intelligence technology. A brain-computer interface (BCI) technology based on EEG-fNIRS multimodal data fusion is proposed to improve the performance and accuracy of EEG-fNIRS multimodal data fusion.

Multimodal data fusion is a hot spot in the field of artificial intelligence in recent years, and its main goal is to effectively combine data or information from different sources to provide a better decision-making basis than a single data source. EEG (computer electroencephalogram) and fNIRS (functional near infrared spectroscopy) are two commonly used brain nerve signal detection techniques, each of which has its own unique advantages and limitations.

EEG can provide high temporal resolution information of cerebral nerve activity, but its spatial resolution is low, while fNIRS can provide high spatial resolution information of cerebral hemodynamics. The development team of WIMI Weimei holography found that the combination of these two technologies can make up for their respective shortcomings and provide more comprehensive and accurate brain neurological information.

WIMI micro-beauty holography uses binary enhancement algorithm to realize the effective fusion of EEG and fNIRS data. This is a deep learning model with self-attention mechanism, which can automatically learn the inherent relevance of data and improve the quality and efficiency of data fusion. In addition, WIMI micro-beauty holography also designed a unique algorithm framework, which can deal with large-scale multi-modal data to meet the application needs of different scenarios.

The process of realizing brain-computer interface (BCI) technology based on EEG-fNIRS multimodal data fusion by WIMI micro-beauty holography can be divided into the following steps:

Data collection: first, you need to use EEG devices and fNIRS devices to collect data from the same target at the same time. EEG devices record brain electrical activity, while fNIRS devices monitor changes in brain blood flow.

Data preprocessing: the collected data need to be preprocessed, and the EEG and fNIRS data are preprocessed, including filtering, denoising, artifact removal and so on, in order to improve the data quality. This usually includes filtering, normalization and other steps. In addition, due to the different time resolutions of EEG and fNIRS devices, time alignment is also needed.

Feature extraction: richer and more accurate features of brain neural activity can be extracted from the fused data. Extract useful features from the preprocessed data. For EEG data, we can extract time domain, frequency domain, time frequency domain and other features, such as average power spectral density, time domain features (such as mean, variance), wavelet transform coefficients and so on. For fNIRS data and luminous flux changes and other characteristics.

Data fusion: in the EEG-fNIRS multimodal data fusion technology, the features are fused to get a comprehensive multimodal feature representation. Multimodal feature fusion mainly combines the features extracted from EEG and fNIRS data to get more comprehensive and accurate brain activity information. Through the binary enhancement algorithm and the deep learning model based on self-attention mechanism, it can automatically learn the inherent relevance of data, thus realizing the effective processing of high-dimensional and complex structure data.

Model training: model training process, the use of cross-validation and other methods for model parameter selection and performance evaluation.

Application implementation: based on the extracted features, achieve a variety of applications. For example, these features are used to train machine learning models to realize the prediction and control of brain neural activity.

Obviously, the brain-computer interface (BCI) technology based on EEG-fNIRS multimodal data fusion developed by NASDAQ:WIMI will provide strong technical support for the research and application in brain science, neuroengineering, clinical medicine and other fields. It can help researchers understand the law of brain neural activity more deeply, provide clinicians with a more accurate basis for diagnosis and treatment, and can also be applied to brain-computer interface, virtual reality and other high-tech fields to promote its technological progress.

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