Classification of Mental Workload Based on Dimension Reduction of PCA in Two Subspaces
With the rapid development of human society so far,a large number of physical labor has been replaced by mechanical engineering,and the task focus of workers has gradually changed from physical labor to mental labor.Real-time evaluation of the operator's mental load to enhance work efficiency is of great significance at present.At present,there are three ways to assess mental load.Some studies have shown that the effect of using bioelectrical signals to classify mental load is more objective than the other two methods.However,the dimension of electroencephalography signal after feature extraction is very high,and the amount of data and computation required is huge,so it needs to be dimensioned.At present,the two most widely used algorithms in dimension reduction are principal component analysis PCA and linear discriminant analysis LDA.In view of the unsupervised nature of PCA and the feature redundancy sensitivity of LDA,a dimensionality reduction algorithm based on two-subspace principal component analysis under two-classification was proposes,which performed principal component analysis on different types of training set data,maps all training set data into the generated space,and then performs PCA-LDA dimensionality reduction again to improve the separability of the data after dimensionality reduction.The experimental results show that the accuracy of the test set of the two-subspace PCA-LDA dimensionality reduction algorithm is higher than that of the single-subspace PCA-LDA algorithm under the two-classification task,which provides a new idea for the field of mental workload classification and high-dimensional data dimensionality reduction.
principal component analysisdata dimension reductionbrain loadEEG signal
万方数据
维普
