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.
The 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.
The fNIRS-MASMOA mainly consists of motion artifact detection,
directional median filter processing and mathematical morphology
optimization correction:
Motion artifact detection: The algorithm first performs
approximate gradient sliding standard deviation calculations on the
original fNIRS signal to detect motion artifacts in the signal.
This step aims to accurately identify types of motion artifacts
such as peaks, baseline mutations and slow drifts.
Directed median filtering processing: Once the motion artifacts
are identified, the algorithm applies directed median filtering
processing for the peaks type of motion artifacts. This processing
method utilizes the gradient information and local features of the
signal to perform directional filtering on the peaks, effectively
removing the interference of peaks on the signal analysis.
Mathematical morphology optimization correction: For motion
artifacts of the baseline mutations and slow drift types, the
algorithm uses mathematical morphology optimization methods for
correction. This is the use of morphology to process the signal in
order to eliminate the effects of baseline mutations and slow
drifts on the signal morphology and features, so as to achieve
accurate reconstruction and optimization of the signal.
The technical framework of WiMi's fNIRS-MASMOA integrates the
directional median filtering algorithm, mathematical morphology
algorithm, and gradient analysis in signal processing to achieve
accurate suppression and optimization of the original signals
through the differential processing of different types of motion
artifacts in the fNIRS signals. The core idea is to adopt specific
processing strategies for targeted correction of different types of
motion artifacts to ensure the accuracy and reliability of the
fNIRS signal data, and to provide an accurate database for the
subsequent analysis of brain functional activities. Its combination
of directional median filtering and mathematical morphology
correction gives full play to the advantages of the two methods,
constructs a comprehensive processing framework, and provides a
comprehensive and efficient solution to the problem of motion
artifacts in fNIRS signals. By effectively suppressing and
correcting the motion artifacts of fNIRS signals, the algorithm is
able to improve the precision and reliability of brain functional
activity analysis, providing a more reliable database for
researchers and medical professionals.
WiMi's fNIRS-MASMOA not only provides a new technique for brain
functional imaging research, but also provides a broader space for
cross-research and application in related fields. It is expected to
promote the expansion of the application of brain functional
imaging technology in cognitive neuroscience, neuroengineering,
neurofeedback and other fields, and bring new development
opportunities for future brain science research and medical
practice.
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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any forward-looking statement except as required under applicable
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SOURCE WiMi Hologram Cloud Inc.