Fatigue state prediction method, device and system based on multi-modal data and storage medium

By constructing a timing connectivity filtering and dynamic brain region enhancement model, the problem of failure to effectively reveal the dynamic changes in the brain in the existing technology is solved, and efficient decoding and accurate identification of fatigue states are achieved.

CN120429666APending Publication Date: 2025-08-05CHENGDU UNIV OF INFORMATION TECH
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Patent Information

Application Number
CN202510517939.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-24
Publication Date
2025-08-05

AI Technical Summary

Technical Problem

The existing technology has failed to effectively reveal the dynamic changes in brain activity, failed to explore channel connectivity changes in timing, and the complex brain mechanism has not been comprehensively explained at the two levels of time series and spatial connectivity.

Method used

The timing connectivity filtering and dynamic brain region enhancement model is constructed, including the frequency band band attention module, the timing connectivity filtering module and the dynamic area enhancement module. By adaptively adjusting the region topology mode, combining the spatiotemporal characteristics of the EEG signal, the timing changes and functional connection strength of the brain are captured.

Benefits of technology

It realizes efficient decoding of fatigue states, reveals the brain's continuous dynamic topological pattern, provides a new basis for fatigue recognition technology, and improves the recognition accuracy of fatigue states.

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Abstract

The invention discloses a fatigue state prediction method, device and system based on multi-modal data and a storage medium, and relates to the technical field of electroencephalogram signal processing, and the method comprises the following steps: constructing a time sequence connectivity filtering and dynamic brain region enhancement model; wherein the time sequence connectivity filtering and dynamic brain region enhancement model comprises a frequency band division band attention module, a time sequence connectivity filtering module and a dynamic region enhancement module; on the basis of the input electroencephalogram data, channel enhancement features are obtained by using a frequency-band-division band attention module; processing the channel enhancement feature by using a time sequence connectivity filtering module to obtain a first feature value; processing the channel enhancement feature by using a dynamic region enhancement module to obtain a second feature value; and fusing the first characteristic value and the second characteristic value, and outputting a prediction result through a full connection layer to obtain a fatigue state prediction result based on the multi-modal data. According to the invention, the dynamic topology mode of the brain area in the fatigue state and the time sequence change of the functional connection strength can be revealed.
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Citation Information

Cited By

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