*Our Contributions*

In this study, the problem of long-term human stress recognition is addressed by using PSS labels and expert evaluation, which has not been explored before. We have hypothesized that wearable sensors (such as those for recording brain activity using EEG electrodes) can be used for identifying chronic stress, without inducing stress using a stimulus. To this end, our experiments have shown that involving a psychology expert for labeling stressed and control subjects is beneficial for such a classification. It is important to note here that we did not use any stimulus in our study to induce stress so that this system can be administered for detecting stress in daily life routine. Two groups of participants were considered including the stressed group and the control group. A total of forty five different features were extracted from EEG signals in frequency domain to classify these two groups. Discriminating features were selected using a statistical significance test. Five different machine learning classifiers including support vector machine (SVM), Naive Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), and multi-layer perceptron (MLP) were used to classify human stress using the selected features. Due to limitations of the data size and the noisy nature of the signals, deep-learning-based systems were not suitable for the task at hand. Therefore, we concentrated on machine learning classifiers that are more suitable for the task that we target to solve. The summary of our findings in this study is as follows:


The rest of the paper is organized as follows: Section 2, describes the related work. Section 3 presents the proposed methodology including data collection, feature extraction, and classification algorithms. Section 4 presents the results and a comparison with previously reported studies. Finally, the conclusion of the study is given in Section 5.

#### **2. Related Work**

Hemispheric specialization is a major concern in neuro-physiological research. Generally, a healthy brain at rest has a fairly balanced level of activity in both hemispheres of brain [20]. The left hemisphere is associated with the processing of positive emotions, while the right hemisphere is associated with the processing of negative emotions [21]. The extent of asymmetry has been suggested to vary under conditions of chronic stress [22]. Frontal asymmetry is highly related to post-traumatic stress disorder (PTSD) [23]. The results in [24], have shown that major depression disorder (MDD) group is significantly right lateralized relative to controls, and both MDD and PTSD displayed more right- than left-frontal activity.

Recently, the feasibility of using EEG in classifying multilevel mental stress has been demonstrated [19], where alpha rhythm at the right pre-frontal cortex was suggested as a suitable bio-marker. A machine learning framework using EEG signals was proposed in [25], where stress was induced by using the Montreal imaging stress task (MIST), and SVM, NB, and LR classifiers were used to classify the stress level of participants. The EEG of participants in resting-state was recorded under negative, positive, and neutral stimulus using soundtracks from the international affective digitized sounds (IADS-2) dataset [26]. Stress detection based on frontal alpha asymmetry was performed using the DEAP dataset, and classification was performed using SVM, KNN, and fuzzy KNN [27]. In [28], a mobile EEG was used to assess stress in humans using EMOTIV EPOC headset in an out-of-lab environment. In an EEG based study, 11 participants were analyzed for the identification of long-term stress [10], including seven mothers of children with mental disability (stress group) and four mothers of healthy children (control group).

A variant of the trier social stress task (TSST) was used to assess stress in 49 participants [29]. Samples of the salivary cortisol and resting state EEG based alpha asymmetry were assessed before and after performing TSST. The frontal and parietal alpha asymmetry was used to classify depression in elderly people [30]. The correlation between frontal and parietal alpha asymmetry, the geriatric depression scale, and the mini mental state examination were analyzed. A high beta activity at the frontal and occipital lobes was observed on the visual input of negative images [31]. The frontal theta activity was shown to decrease due to a stressful mental arithmetic task [32]. In [33], low beta waves in closed eye condition were found to be a strong predictor of perceived stress, where PSS score was predicted by using multiple linear regression. The pre-frontal relative gamma power i.e., the ratio of gamma band and slow brain rhythms, was proposed as a bio marker for identification of stress [34,35].

The related studies presented here can be grouped as either short-term or long-term stress assessment. Short-term stress is measured using a stress eliciting task, while long-term stress is measured without performing any additional mental task. Different techniques have been adopted to measure stress, but most of these techniques require human intervention. Among different physiological measures, EEG has the potential to be used as a measure of stress in daily life. This is due to the fact that EEG headsets are becoming commercially available for observing brain activity in an easy to wear and cost effective manner. The proposed study uses EEG signals acquired with a commercially available EEG headset to identify baseline or long-term stress without relying on stress-inducing tasks.
