BEIJING, Nov. 28,
2023 /PRNewswire/ -- WiMi Hologram Cloud Inc.
(NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram
Augmented Reality ("AR") Technology provider, today 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.
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.
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.
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.
Safe Harbor Statements
This press release contains "forward-looking statements" within
the Private Securities Litigation Reform Act of 1995. These
forward-looking statements can be identified by terminology such as
"will," "expects," "anticipates," "future," "intends," "plans,"
"believes," "estimates," and similar statements. Statements that
are not historical facts, including statements about the Company's
beliefs and expectations, are forward-looking statements. Among
other things, the business outlook and quotations from management
in this press release and the Company's strategic and operational
plans contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
Further information regarding these and other risks is included
in the Company's annual report on Form 20-F and the current report
on Form 6-K and other documents filed with the SEC. All information
provided in this press release is as of the date of this press
release. The Company does not undertake any obligation to update
any forward-looking statement except as required under applicable
laws.
Contacts
WIMI Hologram Cloud Inc.
Email: pr@wimiar.com
TEL: 010-53384913
ICR, LLC
Robin Yang
Tel: +1 (646) 975-9495
Email: wimi@icrinc.com
View original
content:https://www.prnewswire.com/news-releases/wimi-announced-an-optimized-classification-based-on-eeg-and-fnirs-301998871.html
SOURCE WiMi Hologram Cloud Inc.