Abstract
In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.
Highlights
Since many industrial accidents occur due to the unsafe behaviors of workers, adequate precautions are needed to analyze in depth the causes of human error
Experimental results show that the proposed genetic algorithm (GA)-based method outperforms principle component analysis (PCA), with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA
This method is useful for visualizing a high-dimensional feature space that is interrelated by finding a principal component (PC) that maximizes the variance of the features
Summary
Since many industrial accidents occur due to the unsafe behaviors of workers, adequate precautions are needed to analyze in depth the causes of human error. Wheeler et al [6] observed a negative emotional state when the right hemisphere activity of the cerebral cortex is active during the stress period, and several studies showed that researchers investigate a correlation between EEG measurements and depression. Based on these studies, Atencio et al [7] used the frontal alpha asymmetry index to obtain the emotional stress state using the EEG public database and classified them using feature extraction and SVM.
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