Electroencephalogram signal calibration method based on fuzzy processing

Abstract

The invention discloses an electroencephalogram signal calibration method based on fuzzy processing. The method comprises the following steps that (1) a plurality of subjects wearing electrode caps take a transportation device, and electroencephalogram signals of the subjects and operation performance data of the transportation device are collected; 2) preprocessing the collected electroencephalogram signals and the operation performance data; 3) segmenting the preprocessed electroencephalogram signals, and calculating manifold features of each electroencephalogram segment; 4) performing fuzzy processing on the preprocessed operation performance data, and calibrating each electroencephalogram fragment by using fuzzy processing result parameters; and 5) inputting the manifold features of the electroencephalogram fragment and the calibration tag into a support vector machine algorithm for training, and calibrating the electroencephalogram fragment to be calibrated by using a support vector machine model. According to the method, the manifold features of the electroencephalogram signals can be extracted and obtained, the corresponding relation between the electroencephalogram signals and the fluctuation of the transportation equipment is established, and the problem of high coupling among various operation performance types of the transportation equipment is effectively solved.

Description

technical field [0001] The invention relates to an electroencephalogram signal calibration method, in particular to an electroencephalogram signal calibration method based on fuzzy processing. Background technique [0002] With the development of psychology, cognitive science, brain science and other fields, the cognitive state and psychological activities of the subjects can be inferred through the collection and analysis of different physiological signals. The operational performance of the product triggers the user's psychological response based on memory associations and cognitive feelings. Therefore, the evaluation of product solutions should take the user's cognitive state and the high-level emotional needs reflected by it into consideration. [0003] At present, there are two main methods for EEG signal calibration in the research of product program evaluation based on neurocognition: one is the EEG signal calibration method based on subjective evaluation, which simpl...

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Problems solved by technology

[0003] At present, there are two main methods of EEG signal calibration in the research of product program evaluation based on neurocognition: one is the EEG signal calibration method based on subjective evaluation, which simply integrates the subject’s cognitive feedback into the EEG signal calibration In the process, but this method can only reflect an overall memory experience state on a coarse time scale, but cannot reflect the performance of product solutions on a fine time scale; the other is an EEG signal calibration method based on objective evaluation. That is, the calibration of EEG signals is realized by evaluating various physical performance indicators of the product, but this method often ignores the strong coupling relationship between physical performance parameters

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