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Title:
WORKING MEMORY TASK MAGNETOENCEPHALOGRAPHY CLASSIFICATION SYSTEM BASED ON MACHINE LEARNING
Document Type and Number:
WIPO Patent Application WO/2024/083059
Kind Code:
A1
Abstract:
Disclosed in the present invention is a working memory task magnetoencephalography (MEG) classification system based on machine learning. The system comprises a MEG data collection module, a MEG data preprocessing module, a MEG source reconstruction module and a machine learning classification module; the MEG data collection module is used to collect MEG data of different working memory task states of a subject; the MEG data preprocessing module is used to control the quality of the MEG data of the different working memory task states, and separate noise and artifacts; the MEG source reconstruction module is used to perform sensor signal analysis and source reconstruction analysis on the data passing through the MEG data preprocessing module; and the machine learning classification module is used to classify tested working memory tasks, with a power time sequence as a feature. The present invention integrates a complete analysis process from preprocessing to source reconstruction of working memory MEG data, and classifies MEG data of working memory tasks, which has important significance for research on working memory decoding and brain memory related mechanisms.

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Inventors:
ZHANG YU (CN)
QIAN HAOTIAN (CN)
SUN CHAOLIANG (CN)
WANG ZHICHAO (CN)
ZHANG HUAN (CN)
JIANG TIANZI (CN)
Application Number:
PCT/CN2023/124641
Publication Date:
April 25, 2024
Filing Date:
October 16, 2023
Export Citation:
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Assignee:
ZHEJIANG LAB (CN)
International Classes:
G06V10/20; A61B5/00; A61B5/245; G06N20/00; G06V10/30; G06V10/764
Foreign References:
CN115359236A2022-11-18
CN115132349A2022-09-30
CN112446264A2021-03-05
CN113160975A2021-07-23
CN114065825A2022-02-18
US20210346096A12021-11-11
Attorney, Agent or Firm:
HANGZHOU QIUSHI PATENT OFFICE CO., LTD. (CN)
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