Automatic mental load identification method and system

Abstract

The invention discloses an automatic mental load identification method and system. The automatic mental load identification method comprises the following steps of acquiring an electroencephalogram signal; carrying out separating on the electroencephalogram signal by adopting an independent component analysis method to obtain a plurality of independent electroencephalogram components; extracting energy features of each independent electroencephalogram component; and inputting the energy features into an SVM classifier to obtain a mental load classification result of the electroencephalogram signal. According to the automatic mental load identification method and system, the identification accuracy of mental load can be improved.

Description

technical field [0001] The invention relates to the field of mental load identification, in particular to a method and system for automatic identification of mental load. Background technique [0002] The existing mental load identification method first filters the collected electroencephalogram (EEG), then extracts the features of the filtered EEG signal, and finally takes the obtained feature vector as input, and uses the Support Vector Machine (SVM) , SVM) to achieve mental workload classification. figure 1 It is a flowchart of an existing mental load identification method. see figure 1 , Step 101 is the original electroencephalogram signal (EEG) collected; Step 102 is the EEG signal preprocessing; Step 103 is the feature extraction; Step 104 is the SVM classifier. [0003] The existing mental load identification methods are all based on the research of EEG signals. The collected original EEG signals are filtered to obtain the filtered EEG signals, and the energy featu...

AI Technical Summary

Problems solved by technology

The channel signal recorded by the EEG electrode is a mixed signal of a group of brain signals. During the mixing process, some brain signal features are covered up. Direct analysis of the mixed signal is not conducive to distinguishing the characteristics of the EEG signal, resulting in a low recognition accuracy.

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