Brain-Computer Interfaces (BCI) constitute an alternative channel of communication between humans and environment. There are a number of different technologies which enable the recording of brain activity. One of these is electroencephalography (EEG). The most common EEG methods include interfaces whose operation is based on changes in the activity of Sensorimotor Rhythms (SMR) during imagery movement, so-called Motor Imagery BCI (MIBCI).The present article is a review of 131 articles published from 1997 to 2017 discussing various procedures of data processing in MIBCI. The experiments described in these publications have been compared in terms of the methods used for data registration and analysis. Some of the studies (76 reports) were subjected to meta-analysis which showed corrected average classification accuracy achieved in these studies at the level of 51.96%, a high degree of heterogeneity of results (Q = 1806577.61; df = 486; p < 0.001; I² = 99.97%), as well as significant effects of number of channels, number of mental images, and method of spatial filtering. On the other hand the meta-regression failed to provide evidence that there was an increase in the effectiveness of the solutions proposed in the articles published in recent years. The authors have proposed a newly developed standard for presenting results acquired during MIBCI experiments, which is designed to facilitate communication and comparison of essential information regarding the effects observed. Also, based on the findings of descriptive analysis and meta-analysis, the authors formulated recommendations regarding practices applied in research on signal processing in MIBCIs.

The interview is still the main and most important tool in psychiatrist's work. The neuroimaging methods such as CT or MRI are widely used in other fields of medicine, for instance neurology. However, psychiatry lacks effective quantitative methods to support of diagnosis. A novel neuroinformatic approach to help clinical patients by means of electroencephalographic technology in order to build foundations for finding neurophysiological biomarkers of psychiatric disorders is proposed. A cohort of 30 right-handed patients (21 males, 9 females) with psychiatric disorders (mainly with panic and anxiety disorder, Asperger syndrome as well as with phobic anxiety disorders, schizophrenia, bipolar affective disorder, obsessive-compulsive disorder, nonorganic hypersomnia, and moderate depressive episode) were examined using the dense array EEG amplifier in the P300 experiment. The results were compared with the control group of 30 healthy, right-handed male volunteers. The quantitative analysis of cortical activity was conducted using the sLORETA source localization algorithm. The most active Brodmann Areas were pointed out and a new quantitative observable of electrical charge flowing through the selected Brodmann Area is proposed. The precise methodology and research protocol for collecting EEG data as well as the roadmap of future investigations in this area are presented. The essential result of this study is the idea proven by the initial results of our experiments that it is possible to determine quantitatively biomarkers of particular psychiatric disorders in order to support the process of diagnosis and hopefully choose most appropriate medical treatment later.

There are still no good quantitative methods to be applied in psychiatric diagnosis. The interview is still the main and most important tool in the psychiatrist work. This paper presents the results of electroencephalographic research with the subjects of a group of 30 patients with psychiatric disorders compared to the control group of healthy volunteers. All subjects were solving working memory task. The digit-span working memory task test was chosen as one of the most popular tasks given to subjects with cognitive dysfunctions, especially for the patients with panic disorders, depression (including the depressive phase of bipolar disorder), phobias, and schizophrenia. Having such cohort of patients some results for the subjects with insomnia and Asperger syndrome are also presented. The cortical activity of their brains was registered by the dense array EEG amplifier. Source localization using the photogrammetry station and the sLORETA algorithm was then performed in five EEG frequency bands. The most active Brodmann Areas are indicated. Methodology for mapping the brain and research protocol are presented. The first results indicate that the presented technique can be useful in finding psychiatric disorder neurophysiological biomarkers. The first attempts were made to associate hyperactivity of selected Brodmann Areas with particular disorders.

The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical activity of participants was recorded using the dense array amplifier. The most frequently active Brodmann Areas were estimated by means of the photogrammetry techniques and source localization algorithms. The analysis was conducted in the full frequency as well as in alpha, beta, gamma, delta, and theta bands. Next the mean electric charge flowing through each of the most frequently active areas and for each frequency band was calculated. The comparison of the results obtained for the subjects and the control groups is presented. The difference in activity of the selected Brodmann Areas can be observed in all variants of the task. The hyperactivity of amygdala is found in both the patients and the control group. It is noted that the somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex play an important role in the decision-making process as well. Some of our results confirm the previous findings in the fMRI experiments. In addition, the results of the electroencephalographic analysis in the broadband as well as in specific frequency bands were used as inputs to several machine learning classifiers built in Azure Machine Learning environment. Comparison of classifiers' efficiency is presented to some extent and finding the most effective classifier may be important for planning research strategy toward finding decision-making biomarkers in cortical activity for both healthy people and those suffering from psychiatric disorders.

Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Source credibility is among the most important aspects of credibility evaluations. One of the most direct ways to understand source credibility is to use measurements of brain activity of humans who make credibility evaluations. This article reports the results of an experiment during which we have measured brain activity during credibility evaluation using EEG. In the experiment, participants had to learn source credibility of fictitious students based on a preparatory stage, during which they evaluated message credibility with perfect knowledge. The experiment allowed for identification of brain areas that were active when a participant made positive or negative source credibility evaluations. Based on experimental data, we modeled and predicted human source credibility evaluations using EEG brain activity measurements with F1 score exceeding 0.7 (using 10-fold cross-validation). We are also able to model and predict message credibility evaluations with perfect knowledge, and to compare both models obtained from a single experiment.