Abstract
This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color–word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode–feature combinations worked broadly well across subjects to distinguish stress states.
Highlights
This work examined the usefulness of scalp electroencephalography (EEG) recorded by non-medical-grade equipment for discriminating cognitive stress states in human subjects
To examine the potential influence of time-trends in the EEG signal unrelated to the heightened stress induced by color–word incongruence, we built null classifiers that attempted to discriminate early from late segments in each congruent session
Under controlled conditions, a portable, low cost-EEG headset can be used to achieve good accuracy in distinguishing two different cognitive stress states on a single-trial basis, using spectral features extracted from individual electrodes (70.7% mean across subjects for electrode Fc5, for example)
Summary
This work examined the usefulness of scalp electroencephalography (EEG) recorded by non-medical-grade equipment for discriminating cognitive stress states in human subjects. The present study was an extension of our prior work [3] investigating the potential and limitations of low-cost EEG headsets for classifying cognitive stress in non-clinical settings. An extensive literature review exploring the definition of stress was presented by Staal [4]. Staal described the two traditional models for stress: stimulus-based and response-based. The stimulus-based model defines stress as the application of certain conditions (“stressors”) that disturb the “normal” functioning of an individual (e.g., time pressures, physical discomfort, and excessive workload). The response-based model defines stress by the pattern of responses (behavioral, cognitive, and affective) that result from exposure to a stressor.
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