Evolutionary inspired approach for mental stress detection using EEG signal

Stress is a pensive issue in our competitive world and it has a huge impact on physical and mental health. Severe health issues may arise due to long exposure of stress. Hence, its timed detection can be helpful in managing stress periods. In this regard, electroencephalogram (EEG) based techniques have been widely explored, as stress severely impact the functioning and structure of brain. These non-invasive methods for stress detection need improvement in terms of predictive accuracy and reliability. In this work, a novel approach for stress detection has been presented using short duration of EEG signal. Entropy based features were extracted from EEG signal decomposed using stationary wavelet transform. Selected features were used for classification using different supervised machine learning algorithms. Further, different evolutionary inspired approaches were deployed to optimize the parameters of support vector machines (SVM) and perform feature weighting, simultaneously. SVM optimized using whale optimization algorithm resulted in an accuracy of 97.2559%. Accurate detection using short duration EEG signal shows potential of this technique for timed and reliable detection of stress.

Automated summary

This study introduces a novel approach for stress detection using entropy-based features extracted from short-duration EEG signals, classified using various supervised machine learning algorithms. By optimizing SVM parameters and feature weighting, the accuracy of stress detection was significantly improved.