A wearable single-channel EEG instrument using standard machine learning classifiers achieved >90% accuracy in detecting human stress in real time.
Does a wearable single-channel EEG instrument accurately detect human stress in real time?
A novel wearable single-channel EEG instrument demonstrated over 90% accuracy in detecting human stress in real time using machine learning classifiers.
A highly wearable single-channel instrument, conceived with off-the-shelf components and dry electrodes, is proposed for detecting human stress in real time by electroencephalography (EEG). The instrument exploits EEG robustness to movement artifacts with respect to other biosignals for stress assessment. The single-channel differential measurement aims at analyzing the frontal asymmetry, a well-claimed EEG feature for stress assessment. The instrument was characterized metrologically on human subjects. As triple metrological references, standardized stress tests, observational questionnaires given by psychologists, and performance measurements were exploited. Four standard machine learning classifiers (SVM, k-NN, random forest, and ANN), trained on 50% of the data set, reached more than 90% accuracy in classifying each 2-s epoch of EEG acquired from the stressed subjects.
Arpaïa et al. (2020) studied Stress. Wearable single-channel EEG instrument vs. Standardized stress tests, observational questionnaires, and performance measurements was evaluated on Accuracy in classifying each 2-s epoch of EEG acquired from stressed subjects. A wearable single-channel EEG instrument using standard machine learning classifiers achieved >90% accuracy in detecting human stress in real time.