Acute Stress Detection Using Recurrence Quantification Analysis of Electroencephalogram (EEG) Signals

In the present work we intend to classify the brain states under physical stress and experimental control conditions based on the nonlinear features of electroencephalogram (EEG) dynamics using support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO). Recurrence Quantification Analysis (RQA) method was employed to quantify the nonlinear features of high-density electroencephalogram (EEG) signals recorded either during instances of acute stress induction or comparison conditions. Four RQA measures, including determinism (DET), entropy (ENTR), laminarity (LAM) and trapping time (TT) were extracted from the EEG signals to characterize the deterministic features of cortical activity. Results revealed that LASSO was highly efficient in classifying the conditions using any one of the selected RQA measures, while SVM achieved accurate classification based solely on ENTR and TT. Among all four measures of non-linear dynamics, ENTR yielded the best overall classification accuracy.

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