EEG signal feature dimension reduction method based on weighted principal component analysis

Abstract

An EEG signal feature dimension reduction method based on weighted principal component analysis comprises: extracting samples of m EEG signals of fatigue driving and dividing the samples into a training set and a test set for training, and obtaining the total classification accuracy A and n different classification accuracies; subtracting the total accuracy rate A from different classification accuracy rates to obtain n difference values; normalizing the n difference values to obtain n weights; constructing a weight diagonal matrix for the n weights; writing samples of the m EEG signals into an m * n-dimensional matrix; multiplying the m * n-dimensional matrix by the diagonal matrix of the weight to obtain weighted EEG signal feature data; calculating and decomposing a covariance matrix toobtain a characteristic value of the covariance matrix and a unitized characteristic vector corresponding to the characteristic value; selecting unitized feature vectors corresponding to the featurevalues of the first k covariance matrixes to be combined to form a mapping matrix; and thus, obtaining dimension-reduced EEG signal characteristic data. According to the invention, the classificationidentification precision is effectively improved and the training time of the identification model is reduced.

Description

technical field [0001] The invention relates to a dimensionality reduction method for EEG signal features. In particular, it relates to a dimensionality reduction method for EEG signal features based on weighted principal component analysis. Background technique [0002] In the past ten years, the number of automobiles in our country has increased dramatically, and traffic accidents have also increased [1]. According to related reports, my country has become one of the countries with frequent traffic accidents. There are many factors leading to traffic accidents, among which driver fatigue driving is the main cause of traffic accidents. Drivers in a fatigued state will experience distraction and reduced thinking activities, which will lead to slow response, decreased vehicle control, and increase the possibility of traffic accidents [2]. Therefore, it is particularly important to accurately and quickly detect the driver's driving fatigue state. Driving fatigue detection ...

AI Technical Summary

Problems solved by technology

However, different features play different roles in the recognition process [10], so each dimension feature cannot be treated equally

From the above analysis, it can be seen that the PCA dimensionality reduction method needs to be further improved and updated.

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