Psychological assessments refer to a range of measures that are used to evaluate an individual's mental health and functioning. This can include self-reporting questionnaires, behavioral assessments, and clinical interviews. Advantages of psychological assessments include their ease of administration, cost effectiveness, and the fact that they can be administered in a variety of settings. Disadvantages of psychological assessments include the potential for response bias and limitations in the interpretation of results. The advantage of self-reported measures is that many psychological assessments for PTSD are self-reported measures, which can be completed in a relatively short amount of time; hence, they are less expensive than diagnostic interviews. Psychological assessments for PTSD often use standardized measures, which help ensure consistent and reliable assessments. Many psychological assessments for PTSD have established validity and reliability. But they may only provide a limited assessment of other factors, such as physical health, that may also impact PTSD symptoms [6, 7].  
Neuroimaging studies refer to the use of various imaging techniques. Disadvantages of neuroimaging studies include the need for specialized equipment and the potential for exposure to ionizing radiation. Neuroimaging studies can provide a direct assessment of brain activity and may help to identify specific brain regions associated with PTSD. Neuroimaging studies provide objective measures, which can help to reduce bias in assessments. They may have the potential to identify biomarkers for PTSD, which can help to diagnose the condition and guide treatment [8].  
An important biomedical application of EEG signals is investigating PTSD cases, which often exhibit sleep disturbances and several symptoms related to a traumatic event [9]. Previously, we employed a time-series Hurst analysis of EEG signals to recognize healthy controls and PTSD subjects. We found the Hurst exponent in channel F3 to be smaller in PTSD subjects compared to healthy controls [1]. In another study, [PERSON] et al. [10] applied non-linear interdependence (NI) determined from EEG data with 16 channels of 18 healthy controls and 18 PTSD cases. This study showed the growth of non-linear functional connectivity in cortical networks in PTSD in channels of F7, F3, T3, C3, T5, and P3 and decreases in F4, C4, P4, and O2 [10].  
In addition to standard statistical methods and feature extraction, machine learning methods are increasingly popular for prediction and modeling in biomedical research. Many machine learning algorithms extract feature importance that indicates the role of variables in prediction models. In addition, some methods can compute indirect feature importance from machine learning models. In [PERSON]'s study [11], Support Vector Machine (SVM) weights show that after treatment, depressive disorder symptoms reduced by half [11].  
The study is organized with three main parts. First, we describe the EEG data that are available for the study. Next, the new analytical approach of k-mean clustering and linear SVM is explained. Finally, we apply this new approach to an EEG study of PTSD and discuss challenges and potential applications.