Conventional machine learning algorithms are widely used in the processing and classification of biomedical data in the literature. However, in recent years, it becomes a necessity to use a multi-layered learning network due to the increase in the resolution and quantity of the data. The concept of deep learning comprises the whole set of applications that separate sub-classes with high performance, especially in the case of the existence of multi-dimensional data and insufficient human-decision making processes. These methods have the ability to examine the inter-departmental interactions of data in depth. Deep learning is superior to conventional machine learning methods because too many observational data are learned based on increasingly complex patterns. In addition, when the data size is enhanced, superiority difference over conventional machine learning methods is remarkably increased.
Electroencephalography (EEG) data can be expressed in high-dimensional, complex and spatial domains. It is suitable for analysis using deep learning-based networks as they have the potential to work with large datasets. Deep learning models developed for the analysis of EEG signals are expected to automate many repetitive cognitive tasks.
This review article covers the general introduction of deep learning architectures used in the field of biomedical and neuroscience, the applications of these architectures on EEG-based analytical tasks, possible difficulties encountered and possible solutions. As a result of a detailed study, it has been seen that deep learning methods extract complex patterns in the clinical or cognitive data and present a strong method to determine the abstract relationships between different types of data. 
This study addresses the needs of deep learning-based applications in clinical practice and neuroscientific research and aims to provide researchers with an overview of the deep learning-based analysis methodology of EEG signals and potential challenges in future research.

The present study examines whether the Positive Psychology course, which is given for 14 weeks and 3 hours per week to students at a university in Istanbul. 417 students participated. The study assessed pre- to post-test modifications in personal and mental wellbeing, happiness, satisfaction of life, emotional and social intelligence, emotional expressions and attachment styles factors. The findings showed that significant differences between male and female gender in emotional and social intelligence. Interestingly, positive psychology course effects the participants negatively in happiness, well-being, and social intelligence, unlike in emotional expression. It was necessary to discuss these results in a new perspective. The point reached at the end of the Positive Psychology course was not a happier life. Also, if the increase in awareness causes someone to find a deeper meaning, then happiness and well-being will be decreased at the beginning.

Repetitive transcranial magnetic stimulation is an effective treatment option for treatment resistant depressive disorder. The presented case suggests that repetitive transcranial magnetic stimulation might be effective in inhibition of binge eating and purging behaviors. This case suggests that left dorsolateral prefrontal stimulation with high frequency repetitive transcranial magnetic stimulation could be a potential treatment option for bulimia nervosa comorbid with depressive disorder

The aim of this study was to examine the validity and the reliability of the Turkish version of ADHD DSM scale. ADHD DSM Scale was applied in a sample of 225 persons. Simultaneously Attention Deficit Hyperactivity Disorder Self-report Scale (ASRS) scale was also applied and DİVA interview was used to ascertain the diagnosis in 45 indiviuals with suspected ADHD. The reliability, concurrrent validity and construct validity which is detected by factor analysis was found to be adequate. The Turkish Version of ADHD DSM Scale is a reliable and valid tool for scanning ADHD symptoms in adults.

This study aimed to investigate the radiation attenuation properties of seven brain regions namely Frontal, Occipital, Parietal, Temporal cortexes, Hippocampus, Thalamus and Cerebellum in terms of their elemental compositions. Monte Carlo N-Particle Transport Code System-extended (MCNPX) version 2.6.0 (Los Alamos National Lab, USA) general purpose Monte Carlo code has been employed in order to calculate the mass attenuation coefficients of those aforementioned brain regions. A satisfactory agreement has been obtained on the mass attenuation coefficients (µ/ρ) calculated by MCNPX and XCOM for those brain regions under investigation. The results underlined that Cerebellum has the highest mass attenuation coefficients in terms of the radiological energy values. This can be explained by the elemental mass fraction value of Chlorine (Cl) in Cerebellum. It could be deduced that the intensity of diagnostic radiation can be more attenuated in Cerebellum than rest of the brain regions during the brain CT or brain PET examinations. The data from the present paper would be useful for the use of standard simulation geometry and mass attenuation coefficients for medical physics as well as the applications of radiation physics.

In the earlier classification of diseases, we had the firm decision that psychopathology can be divided into two main groups:
Neurosis and Psychosis. But we are quite convinced that there are blur areas in between that can perfectly be defined as “partialsegmental
psychosis”. In the paper we discussed the topics.

Fear of Missing Out (FOMO) is an emerging concept related to internet and social media usage. FOMO is described as feelinganxiety when the person does not use social media. Studies showed that FOMO affects well-being negatively. In this study, weaimed to build a Turkish scale for measuring FOMO. For this, 31 items were prepared and in the final scale 22 items were retained.The reliability and validity measures were found to be adequate. For the future studies, we recommend to test the scale also withthe adolescent population and to explore the relationship between FOMO and well-being.

Flow state emerges when a person engages in autotelic activities, which are both enjoyable and challenging. While studies generally focused on qualitative data of flow state, a few were conducted on psychophysiological basis of it. Present study aimed to investigate EEG correlates of flow. Twenty participants preliminarily filled out Flow Short Scale Turkish Version and completed a ping-pong game at two levels (slow-boring and fast-flow) during EEG recording. The results revealed that theta power was significantly greater for all regions during flow condition compared to non-flow condition and delta power was significantly greater during flow on central and parietal regions. There is no difference between flow/non-flow conditions for coherence. A positive correlation was found between delta and theta powers and subscales scores of Flow Short Scale. The increases in these theta and delta frequency bands could be important indicators of flow state. The coherence results revealed that interhemispheric synchronization was not modified by flow. If confirmed with multiple tasks and in clinical groups, EEG correlates of flow state could be useful increase performance and well-being.

In the present paper, we discussed the insufficiencies of two-dimensional (2D) confrontations and proposed the utility of threedimensional (3D) and even four-dimensional (4D) confrontations, in researches specially of mono-symptomatic psychopathological cases like for instance in pedophilia.

This is an approach proposal to the assessment and extinction predictability, through a “Brain electro-Activity Map” to Culture, Biases, Brain Washing, Behavior Changing, Public Opinion and similar.
In this paper, we tried to propose an approach through a “Brain Electro Activity Map” to predict the assessment and modification possibility of Culture, Biases, Brain Washing, Behavior Changing, Public Opinion and similar. Because according our opinion, technically there is not any Neuro-Physiological difference between their acquisition and extinction.
We introduced some new concepts which are “flexophrenia-sclerophrenia” and “facilitated and automated connectomes” which are always in need of expression-activity with strongly invasive components.
Then by the combination of the above three concepts, with the well-known old “need of stimulation” (which is an implicit form of accepting the presence of a ”need of activity-expression”), we asserted that all the above mentioned behavioral patterns’ onsets, durations, resistances, changes, can be approached, predicted and changed, in a similar way to each other’s and, can be traced as well, through the implementation of the “Brain Electro Activity Map”