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WiMi Announced Motion Artifact Suppression and Morphology Optimization Algorithm for fNIRS Signals
BEIJING, Dec. 4, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR")
About this update from Wimi Hologram Cloud Inc.
[{"type":"text","content":"BEIJING, Dec. 4, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (\"WiMi\" or the \"Company\"), a leading global Hologram Augmented Reality (\"AR\") Technology provider, today announced that a new motion artifact suppression and morphology optimization algorithm is developed for motion artifacts such as peaks, baseline mutations and slow drifts in fNIRS signal processing based on mathematical morphology and median filtering methods. The algorithm makes full use of mathematical morphology methods to analyze and optimize the signal morphological features, and combines the advantages of median filtering algorithms for improvement, in order to enhance the ability of accurate identification and effective correction of motion artifacts in fNIRS signals, and to provide strong support for the accurate interpretation of brain functional activities.\n\nThe core of the algorithm is the strategy of integrated motion artifact suppression and morphological optimization. First, by calculating the approximate gradient sliding standard deviation of the signal, WiMi's motion artifact suppression and morphological optimization algorithm for fNIRS signals (fNIRS-MASMOA) is capable of detecting the presence of motion artifacts, and then applying specific processing methods for different types of artifacts and then applies specific processing methods for different types of artifacts. For peaks, the algorithm uses an improved median filtering technique to remove them efficiently, and a mathematical morphology approach to optimize the shape of the signal through morphological manipulation to make baseline mutations and slow drifts more consistent with the true characteristics of brain activity. Compared to existing methods, fNIRS-MASMOA demonstrates excellence in terms of mean square error, signal-to-noise ratio, squared Pearson correlation coefficient, and peak-to-peak error. This algorithm represents a milestone in providing researchers with a new and efficient tool to study brain activity more accurately.\nThe fNIRS-MASMOA mainly consists of motion artifact detection, directional median filter processing and mathematical morphology optimization correction:\nMotion artifact detection: The algorithm first performs approximate gradient sliding standard deviation calculations on the original fNIRS signal to detect motion artifacts in the si...