Concentration level multi-modal prediction method and system, storage medium, and electronic device

By combining multimodal signals for attention prediction, the problems of insufficient objectivity, discontinuity, and low robustness in existing technologies are solved, enabling real-time, accurate monitoring and personalized feedback of attention, thereby improving work efficiency and learning outcomes.

WO2026061013A1PCT designated stage Publication Date: 2026-03-26SHANGHAI PROSPECTIVE INNOVATION RES INST CO LTD
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Filing Date
2025-06-10
Publication Date
2026-03-26

AI Technical Summary

Technical Problem

Existing attention prediction methods lack objectivity, are inconsistent, exhibit significant individual differences, and have low robustness, making it difficult to effectively monitor and assess individual attention in complex environments.

Method used

By combining multimodal information such as EEG signals, ECG signals, EEG signals, and physiological signals, attention prediction is performed using a deep learning model through preprocessing and feature extraction. This includes bandpass filtering, baseline drift removal, short-time Fourier transform, and multimodal feature extraction. Models such as SVM, decision tree, random forest, CNN, LSTM, or EEGNet are used for training.

Benefits of technology

It enables real-time and continuous monitoring of focus, improves the accuracy and robustness of predictions, provides personalized feedback to individuals, and enhances work efficiency and learning outcomes.

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Abstract

The present invention provides a concentration level multi-modal prediction method and system, a storage medium, and an electronic device. The method comprises the following steps: acquiring multi-modal information of a subject, wherein the multi-modal information comprises an electroencephalogram signal, an electrocardiogram signal, an electrooculogram signal, and a physiological signal, and the physiological signal comprises one or more of a heart rate signal, a blood oxygen signal, and a pulse signal; preprocessing the multi-modal information to obtain a multi-modal feature of the multi-modal information; and training a concentration level prediction model on the basis of the multi-modal feature, so as to implement concentration level prediction on the basis of the trained concentration level prediction model. The concentration level multi-modal prediction method and system, the storage medium, and the electronic device of the present invention achieve accurate concentration level prediction by combining the multi-modal information such as the electroencephalogram signal, the electrocardiograph signal, and the electrooculogram signal.
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Citation Information

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