*4.6. Discussion*

Numerous studies have analyzed brain activities under stressful conditions, which are induced by a task such as impromptu speech, examination, mental task, public speaking, and the cold pressor test [38–43]. These studies evaluate short-term induced stress, whereas the classification of long-term stress using EEG has not been widely investigated. In Table 7, studies involving EEG to classify human stress are presented for comparison. It is observed that different stress-inducing tasks were used such as driving simulation, examination, and mental arithmetic tasks. Specialized instruments like MIST and Stroop tests were also used to induce stress. For chronic stress there could be several stressors that affect the physical, emotional, cognitive, or behavioral well being of a human being. Therefore, it is proposed that recording resting state EEG for stress classification is a better choice without involving stress induction. The number of participants involved in such studies vary from 5 to 42. The SVM and NB were used as classifiers in most of the studies. SVM was found to be the most efficient classifier, giving a maximum accuracy of 96%, when stress was induced by mental arithmetic test. In [10], the resting state EEG was recorded for two minutes and a nonlinear analysis was performed but no classification algorithm was used. In [44], chronic stress has been classified with an accuracy of 90%, using EEG recordings from eight electrodes and a stress-inducing condition.

Despite the difficulties of EEG in stress studies, there are cases where the use of EEG is vital and it has a clinical meaning in various conditions. For instance, ECG is not a direct stress measurement system, especially when mental stress originates in the brain. Furthermore, we studied long-term stress, and we did not have any stress inducer in our study (unlike other ECG- and HRV-based studies); hence, EEG can be a modality of choice for our experiments and we show its effectiveness with our experimental results. Although EEG has not been widely used for long term stress classification in clinical practice, our proposed method attempts to establish this approach. It has been shown that conditions such as anxiety, tension, and depression decrease as the frontal asymmetry shifts to the right hemisphere of the brain giving significance to EEG laterality [22]. It was demonstrated that variations in the beta activity [31] and pre-frontal gamma [34] contribute towards stress assessment. Hence there is evidence suggesting that these oscillations in the pre-frontal brain region can be used for assessment of stress using EEG recordings.

It is shown in this study that the alpha asymmetry of the brain can be considered as a potential marker for the recognition of chronic stress in humans. We observed (Table 5) that the classification accuracy using beta and gamma oscillations was lower when compared to alpha asymmetry. Whenever a combination of alpha asymmetry from beta and gamma oscillations was used, the decision boundaries were changed. Due to this, the classification accuracy was lower when compared to the case when alpha asymmetry was individually used as a feature. The labeling should be performed by using a hybrid method (psychology expert and PSS scores) for training the system in a supervised manner. Due to the limited size of the data, we have shown that MLP is the only class of neural network based classifier that can fit to the task of stress classification. For more deeper networks, we would need more instances of EEG recordings.


**Table 7.** Comparison of results with previously related EEG-based studies.
