WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that by integrating data from EEG and fNIRS and using machine learning algorithms for classification optimization, the complementarity between EEG and fNIRS not only improves the accuracy and spatial resolution of brain activity recognition, but also provides more comprehensive data support for neuroscience research.
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WiMi’s classification optimization based on EEG and fNIRS mainly includes the key steps of data acquisition and pre-processing, signal fusion and feature extraction, feature weighting and optimization, classifier design and training, and result analysis and optimization. This achieves data fusion and feature extraction by comprehensively utilizing the complementary advantages of EEG and fNIRS signals, and then adopts the weighted optimization method to strengthen the classification effect of features, and designs the classifier model using machine learning algorithms for training and optimization. Finally, the performance and stability of the classifier are improved through the analysis and optimization of the classifier training results. Key components include:
Data acquisition and pre-processing: By acquiring and pre-processing EEG and fNIRS signals. This uses specialized instrumentation for the acquisition of brain activity signals, and pre-processing techniques to filter, denoise, and correct the raw data to eliminate interference and noise, ensuring the reliability and accuracy of subsequent analyses.
Signal fusion and feature extraction: The pre-processed EEG and fNIRS signals are fused and key features are extracted. Fusion includes signal fusion algorithms based on time series, and spatial information fusion techniques. The feature extraction process may involve features extracted from different perspectives, such as spectral features, time-domain features, and spatial distribution features, in the time domain, frequency domain, or spatial domain.
Feature weighting and classifier design: The extracted features are weighted to improve the accuracy of the classifier. Attribute weighting methods based on k-Means clustering or difference-based attribute weighting method techniques are used. Features can be weighted according to their importance to improve the recognition of different features and thus improve the overall classifier performance.
Classifier training and validation: Using the weighted and optimized feature data, appropriate classification models are built, including linear discriminant analysis (LDA), support vector machine (SVM) and k nearest neighbor algorithm (kNN). The performance and accuracy of the classifiers are evaluated by training and validating the data in the training and validation sets to ensure their recognition and generalization of brain activities.
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Result analysis and optimization module: Based on the training results of the classifiers, the algorithms and models are analyzed, and the parameters are further optimized and adjusted to improve the performance of the classifiers. By comparing the effects of different weighting methods and classifiers, the optimal solution is selected and further improvement of the algorithm is carried out to meet the needs of specific application scenarios.
WiMi’s EEG and fNIRS-based classification optimization aims to give full play to the complementary advantages of EEG and fNIRS signals, and improve the classification and recognition accuracy of brain activities through reasonable data processing and analysis methods. The cross-fertilization of the fields of neuroscience and artificial intelligence in this technical approach suggests that AI algorithms play an increasingly important role in neuroscience research. Combining machine learning algorithms with brain activity data analysis, can provide richer and more accurate data support for the development of artificial intelligence technology.
The development of WiMi’s EEG- and fNIRS-based classification optimization has brought new possibilities for the application of brain-computer interface technology. The breakthrough in this technology enables brain-computer interface devices to more accurately interpret brain activity and translate it into specific commands or operations, providing a more convenient and efficient way of human-computer interaction.
Overall, classification optimization based on EEG and fNIRS is of great significance and broad prospects in the fields of neuroscience research, artificial intelligence development and medical diagnosis, and its development will bring breakthroughs in the understanding and enhancement of human cognitive abilities. Providing a more accurate and reliable means of analyzing brain activity, helps to explore the working mechanism of the human brain and cognitive processes in greater depth. Through in-depth study of the association between brain activity patterns and cognitive functions, can provide richer data support for cognitive neuroscience research and promote the continuous development of neuroscience.
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