Mental load detection method

A technology of mental load and detection method, applied in the intersection of neuroengineering and human factors engineering, can solve problems such as application limitation and limited amount of information, and achieve the effect of avoiding negative effects, reducing negative effects, and reliable and stable detection methods.

Active Publication Date: 2012-10-10
TIANJIN UNIV
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Problems solved by technology

Among them, psychophysiological signals assume that changes in mental load will cause changes in these physiological indicators, but many other factors that have nothing to do with mental load may also cause these changes, and the amount of information contained in these signals is limited, and the application is limited

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Embodiment Construction

[0031] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0032] In order to realize the online detection of mental load and expand the scope of application, see figure 1 , figure 2 , image 3 and Figure 4 , the embodiment of the present invention provides a mental load detection method, see the following description for details:

[0033]In recent years, research on the combination of EEG and functional near-infrared spectroscopy has also been carried out in brain cognition, because the combination of the two can provide more information on brain activity and make up for their respective shortcomings, thus improving the reliability and reliability of the system. Stability, improving the accuracy of mental workload detection. Here, the method of combining EEG and brain functional near-in...

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Abstract

The invention discloses a mental load detection method. The method comprises the following steps of: extracting linear feature parameters and non-linear feature parameters of brain electric signals; extracting blood oxygen saturation degree indexes from brain near infrared spectrum signals, and taking the blood oxygen saturation degree indexes as blood oxygen saturation degree feature parameters; averaging the performance indexes of all sections of auxiliary task, acquiring the performance index of average auxiliary task, finding features sensitive to the performance index of average auxiliary task from the linear feature parameters, the non-linear feature parameters and the blood oxygen saturation degree index parameters, and acquiring sensitive features; establishing a mental load detection model according to the sensitive features and the performance index of the average auxiliary task by adopting a support vector regression or artificial neural network; and outputting mental load indexes through the mental load detection model, regulating the task of operators so that the mental load indexes are within the preset range, thus finishing the flow. The method implements the on-line detection of the mental load and expands the application range.

Description

technical field [0001] The invention belongs to the intersecting field of neural engineering and human factor engineering, and particularly relates to a method for detecting mental load by means of neural engineering. Background technique [0002] Mental Workload (Mental Workload) is an important research topic in the field of human factors engineering. Man-machine systems have experienced the development process from manual work to automated systems, which reduces physical labor and increases mental work in modern man-machine systems. Operational jobs are transformed into knowledge-based jobs. Therefore, the proportion of mental workers in the employed population is increasing, and the proportion of mental labor in the labor structure is also increasing. Studies have shown that excessive mental load can lead to rapid reduction of attention, increased mistakes, rapid brain fatigue, reduced flexibility, stress response, and errors in information acquisition and analysis and ...

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Application Information

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IPC IPC(8): A61B5/00A61B5/0476A61B5/1455
Inventor 柯余峰明东李南南陈龙张迪许敏鹏綦宏志万柏坤
Owner TIANJIN UNIV
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