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The guided imagery training is considered as an effective method and therefore widely used in modern cognitive psychotherapy, while less is known about the effectiveness of self-guided. The present study investigated the effects of regular use of self-guided positive imagery, applying both subjective (assessment of the psycho-emotional state) and objective (electroencephalographic, EEG) approaches to research. Thirty healthy subjects participated in the cognitive imagery-training program for 12 weeks. The schedule began with group training with an instructor for 2 days, where the participants learned various techniques of positive imagery, after which they continued their individual training at home. Psychological and EEG evaluations were applied at the baseline and at the end of the training period. The impact of training on the psycho-emotional states of the participants was evaluated through: Center for epidemiologic studies- Depression (CES-D) 20 item scale, Satisfaction with life scale (SWLS) and General Self-Efficacy scale (GSE). EEGs (19-channels) were recorded at rest with eyes closed. EEG analysis was performed using Low resolution electromagnetic tomography (LORETA) software that allows the comparison of current source density (CSD) and functional connectivity (lagged phase and coherence) in the default mode network before and after a workout. Initial assessment with CES-D indicated that 22 participants had subthreshold depression. After the training participants had less prominent depressive symptoms (CES-D, p = 0.002), were more satisfied with their lives (SWLS, p = 0.036), and also evaluated themselves as more effective (GSE, p = 0.0002). LORETA source analysis revealed an increase in the CSD in the right mPFC (Brodmann area 10) for beta-2 band after training (p = 0.038). LORETA connectivity analysis demonstrated an increase in lagged coherence between temporal gyruses of both hemispheres in the delta band, as well as between the Posterior cingulate cortex and right BA21 in the theta band after a workout. Since mPFC is involved in emotional regulation, functional changes in this region can be seen in line with the results of psychological tests and their objective validation. A possible activation of GAMK-ergic system is discussed. Self-guided positive imagery (after instructions) can be helpful for emotional selfregulation in healthy subjects and has the potential to be useful in subthreshold depression.

Traumatic Brain Injury (TBI) is recognized as an important cause of death and disabilities after an accident. The availability a tool for the early diagnosis of brain dysfunctions could greatly improve the quality of life of people affected by TBI and even prevent deaths. The contribution of the paper is a process including several methods for the automatic processing of electroencephalography (EEG) data, in order to provide a fast and reliable diagnosis of TBI. Integrated in a portable decision support system called EmerEEG, the TBI diagnosis is obtained using discriminant analysis based on quantitative EEG (qEEG) features extracted from data recordings after the automatic removal of artifacts. The proposed algorithm computes the TBI diagnosis on the basis of a model extracted from clinically-labelled EEG records. The system evaluations have confirmed the speed and reliability of the processing algorithms as well as the system's ability to deliver accurate diagnosis. The developed algorithms have achieved 79.1% accuracy in removing artifacts, and 87.85% accuracy in TBI diagnosis. Therefore, the developed system enables a short response time in emergency situations and provides a tool the emergency services could base their decision upon, thus preventing possibly miss-diagnosed injuries.

Although Traumatic Brain Injury (TBI) is recognized as a major public health concern, there is currently no efficient method of fast and reliable detection of mild TBI at the point of need, where the injury has occurred. This paper addresses this problem by proposing a portable system for emergency TBI diagnosis and monitored personalized treatment based on quantitative electroencephalography (qEEG) and High Definition transcranial Electrical Stimulation (HD-tES). The paper highlights three innovative elements of the proposed system: its architecture, communication framework, and diagnostic process for detecting TBI in emergency situations.

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