Schizophrenia is an endogenous mental disorder leading to disability. Timely treatment is crucial for mitigating long-term negative effects. In this study, event-related potentials (ERPs) obtained from EEG data of patients and healthy individuals performing a modified visual Go/NoGo task were analyzed. We used the method of blind source separation (BSS) based on second-order statistics to decompose ERPs into functionally distinct components. Various features were extracted from these latent components within specific time windows for use in machine learning models. The Support Vector Machine (SVM) model achieved sensitivity and specificity of 96.7% and 97.7%, respectively. Using SHAP method, the most significant features were identified to understand the model's decision-making factors. These factors were validated by experts in physiology, aligning with their expectations.

The parietal P300 wave of event-related potentials (ERPs) has been associated with various psychological operations in numerous laboratory tasks. This study aims to decompose the P3 wave of ERPs into subcomponents and link them with behavioral parameters, such as the strength of stimulus-response (S-R) links and GO/NOGO responses. EEGs (31 channels), referenced to linked ears, were recorded from 172 healthy adults (107 women) who participated in two cued GO/NOGO tasks, where the strength of S-R links was manipulated through instructions. P300 waves were observed in active conditions in response to cues, GO/NOGO stimuli, and in passive conditions when no manual response was required. Utilizing a combination of current source density transformation and blind source separation methods, we decomposed the P300 wave into two distinct components, purportedly originating from different parts of the parietal lobules. The amplitude of the parietal midline component (with current sources around Pz) closely mirrored the strength of the S-R link across proactive, reactive, and passive conditions. The amplitude of the lateral parietal component (with current sources around P3 and P4) resembled the push-pull activity of the output nuclei of the basal ganglia in action selection-inhibition operations. These findings provide insights into the neural mechanisms underlying action selection processes and the reactivation of S-R links.

Objective. The spatial resolution of event-related potentials (ERPs) recorded on the head surface is quite low, since the sensors located on the scalp register mixtures of signals from several cortical sources. Bayesian models for multi-channel ERPs obtained from a group of subjects under multiple task conditions can aid in recovering signals from these sources. Approach. This study introduces a novel model that captures several important characteristics of ERP, including person-to-person variability in the magnitude and latency of source signals. Furthermore, the model takes into account that ERP noise, the main source of which is the background electroencephalogram, has the following properties: it is spatially correlated, spatially heterogeneous, and varies over time and from person to person. Bayesian inference algorithms have been developed to estimate the parameters of this model, and their performance has been evaluated through extensive experiments using synthetic data and real ERPs records in a large number of subjects (N = 351). Main results. The signal estimates obtained using these algorithms were compared with the results of the analysis of ERPs by conventional methods. This comparison showed that the use of this model is suitable for the analysis of ERPs and helps to reveal some features of source signals that are difficult to observe in their mixture signals recorded on the scalp. Significance. This study shown that the proposed method is a potentially useful tool for analyzing ERPs collected from groups of subjects in various cognitive neuroscience experiments.

The aim of the investigation was to study the features of brain activity when comparing visual stimuli under conditions of delayed motor response. Event-related potentials (ERPs) were studied in 84 healthy subjects in a three-stimulus test, the first two stimuli were a comparison pair, and the third stimulus triggered a motor response. After presentation of the second stimulus ERPs were recorded: a complex of two waves with occipital (Oz, most pronounced in the interval 100–150 ms) and posterior temporal localization (P7, P8, 190–270 ms); negative wave in the frontal regions (Fz, 240–300 ms) and positive wave in the parietal regions (Pz, 270–450 ms). Brain responses differ in amplitude in case of a match and discrepancy of visual stimuli. The article discusses the physiological meaning of these waves and their differences under two conditions.

OBJECTIVE:To study the impairment of cognitive functions in patients with different stages of the burnout syndrome (BS).MATERIAL AND METHODS:78 patients aged 25-45 years (average age 36.9±9.5 years) were examined, which at the BS stage were divided into two subgroups: Residence (51.3%, n=40) and Exhaustion (48.7%, n=38). The control group consisted of 106 practically healthy (average age 36.3±7.2 years) The following methods were used: Russian-language version of the MBI questionnaire, questionnaire to diagnose the level of emotional burnout by V.V. Boyko, questionnaire CFQ, method «Learning 10 words» by A.R. Luria, registration of cognitive evoked potentials (EP) in the psychophysiological visual test VCPT.RESULTS:Subjective symptoms of memory loss were in 47 patients (60.3% of the total number of patients with EBS): 17 patients (42.5%) from the subgroup Resistance and 30 patients (78.9%) from the subgroup Exhaustion. The quantitative evaluation of the subjective symptoms in the CFQ test showed a reliable increase in all patient groups (p<0.05) and especially in the subgroup Exhaustion. There was statistically reliable decrease of the P200 component in subgroup Resistence and control group in the alloys Cz (p<0.001) and Fz (p<0.001), as well as statistically reliable reduction of the P300 component in the indicated leads (Cz (p<0.001) and Pz (p<0.001)) in patients in the subgroup Resistance. Most BS patients had cognitive complaints that were more common at the Exhaustion stage. At the same time, objective cognitive impairments were detected only in patients at the stage of Exhaustion. Only the long-term memory is affected. Psychophysiological research has shown a decrease in the level of attention in both subgroups, which demonstrated an increased impairment of mental processes.CONCLUSION:Cognitive impairment in patients with BS manifests in various forms of attention, memory impairment, and performance degradation in the resistance and exhaustion phases, and can result from high asthenization.

Event-related potentials (ERPs) recorded on the surface of the head are a mixture of signals from many sources in the brain due to volume conductions. As a result, the spatial resolution of the ERPs is quite low. Blind source separation can help to recover source signals from multichannel ERP records. In this study, we present a novel implementation of a method for decomposing multi-channel ERP into components, which is based on the modeling of second-order statistics of ERPs. We also report a new implementation of Bayesian Information Criteria (BIC), which is used to select the optimal number of hidden signals (components) in the original ERPs. We tested these methods using both synthetic datasets and real ERPs data arrays. Testing has shown that the ERP decomposition method can reconstruct the source signals from their mixture with acceptable accuracy even when these signals overlap significantly in time and the presence of noise. The use of BIC allows us to determine the correct number of source signals at the signal-to-noise ratio commonly observed in ERP studies. The proposed approach was compared with conventionally used methods for the analysis of ERPs. It turned out that the use of this new method makes it possible to observe such phenomena that are hidden by other signals in the original ERPs. The proposed method for decomposing a multichannel ERP into components can be useful for studying cognitive processes in laboratory settings, as well as in clinical studies.

Objectives. To study the characteristics of cognitive dysfunction in patients at various stages of emotional burnout syndrome (EBS). Materials and methods. A total of 78 patients aged 25–45 (mean 36.9 ± 9.5) years took part in the study and were divided into Resistance (51.3

Schizophrenia is a major psychiatric disorder that significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. In this paper, a machine learning based diagnostics of schizophrenia was designed. Classification models were applied to the event-related potentials (ERPs) of patients and healthy subjects performing the visual cued Go/NoGo task. The sample consisted of 200 adult individuals ranging in age from 18 to 50 years. In order to apply the machine learning models, various features were extracted from the ERPs. The process of feature extraction was parametrized through a special procedure and the parameters of this procedure were selected through a grid-search technique along with the model hyperparameters. Feature extraction was followed by sequential feature selection transformation in order to prevent overfitting and reduce the computational complexity. Various models were trained on the resulting feature set. The best model was support vector machines with a sensitivity and specificity of 91% and 90.8%, respectively.

Symptoms in patients with obsessive-compulsive disorder (OCD) are associated with impairment in cognitive control, attention, and action inhibition. We investigated OCD group differences relative to healthy subjects in terms of event-related alpha and beta range synchronization (ERS) and desynchronization (ERD) during a visually cued Go/NoGo task. Subjects were 62 OCD patients and 296 healthy controls (HC). The OCD group in comparison with HC, showed a changed value of alpha/beta oscillatory power over the central cortex, in particular, an increase in the alpha/beta ERD over the central-parietal cortex during the interstimulus interval (Cue condition) as well as changes in the postmovement beta synchronization topography and frequency. Over the frontal cortex, the OCD group showed an increase in magnitude of the beta ERS in NoGo condition. Within the parietal-occipital ERS/ERD modulations, the OCD group showed an increase in the alpha/beta ERD over the parietal cortex after the presentation of the visual stimuli as well as a decrease in the beta ERD over the occipital cortex after the presentation of the Cue and Go stimuli. The specific properties in the ERS/ERD patterns observed in the OCD group may reflect high involvement of the frontal and central cortex in action preparation and action inhibition processes and, possibly, in maintaining the motor program, which might be a result of the dysfunction of the cortico-striato-thalamo-cortical circuits involving prefrontal cortex. The data about enhanced involvement of the parietal cortex in the evaluation of the visual stimuli are in line with the assumption about overfocused attention in OCD.

This study presents a comparison of the effect on EEG electrical activity in the range of infraslow frequencies of two methods: infra-low frequency EEG biofeedback and heart rate variability training. The study involved 17 healthy subjects aged 21 to 50 years with minor symptoms of a physiological or psychological nature, who did not have a history of neurological or psychiatric diseases. To evaluate the results of the training, we analyzed the spectral power of slow EEG oscillations during the performance of the attention test (Visual Go/NoGo), recorded before and after twenty sessions of biofeedback. Both the subjective assessment of the physiological and psychological state and the results of the visual test showed more pronounced positive changes under the influence of EEG biofeedback compared to the cases of heart rate variability training. A significant increase in the amplitudes of oscillations in the infraslow EEG range was observed only after EEG biofeedback.

We studied the effect of the complexity of the secondary task on event-related EEG dynamics over the sensorimotor cortex in primary task under multitasking conditions. 32-channel EEG was recorded from 24 healthy subjects during the performance of four tests combining Go/NoGo and N-back paradigms and differing in complexity. In contrast to the previous studies using the classical paradigm of N-back tasks requiring memorization of information about the stimuli itself, for this study we developed a variant of the N-back task, which requires memorization of the completed action. It was shown that an increase in the complexity of the N-back task leads to a decrease in the magnitude of beta synchronization observed after a movement in the Go condition of the Go/NoGo task over the left sensorimotor cortex (electrode C3). It can be assumed that an increase in working memory load, regardless of the type of information held in memory, leads to a deterioration in the processes of movement control necessary to perform the primary task. The obtained results support the assumption that the multitasking condition requires the distribution of attention resources and, due to this, decreases the quality of each task performance.

The aim of the present work was to develop a Bayesian probabilistic model for parallel factor analysis of event-related potentials (ERP) in the human brain. Twelve statistical models considering the specific features of signals from ERP sources are proposed. Procedures for constructing sets of random parameter values based on Markov chain Monte Carlo methods were developed for these models. The effectiveness of these procedures was evaluated using both synthetic data with different signal:noise ratios and a set of ERP recordings obtained from 351 people in a Go/NoGo test. The procedure yielding the most accurate parameter assessments for models was selected. Analysis of the relationship between signals in the model and the type of activity performed by human subjects showed that Bayesian parallel factor analysis identifies functional differences between ERP components.

It is well known that rhythmic light stimulation can alter the electrical activity of the human and animal brain. Moreover, the brain response to certain flicker frequencies significantly exceeds the responses to neighboring frequencies. This phenomenon is thought to be related with the effect of resonance, as evidenced by the coincidence of one of the maxima in the profile of the response to flashes with the frequency of the alpha rhythm. However, other frequencies that cause an increased response to flashes are not reflected in electroencephalogram (EEG) as dominant oscillations. The goal of this study was to reveal the relationship between local maxima in the profile of the responses to flashes of different frequencies and dominant brain oscillations recorded in electrocorticogram (ECoG) of rhesus monkeys without stimulation. The study was carried out on four male Macaca mulatta individuals. In three animals, peak responses were elicited by flickers at 8 and 16 Hz, while one monkey showed a second peak in the 22−30 Hz range. The first maximum (8−10 Hz) in the profile of the response to rhythmic photostimulation coincided with the dominant rhythm recorded in the occipital and parietal regions at rest. The second maximum at 16 Hz coincided with the dominant ECoG rhythm in one of the primates when it was in the state of emotional arousal, which may account for the resonant origin of the increase in responses in this frequency range. The data obtained indicate that dominant brain rhythms, including latent rhythms revealed only by rhythmic photostimulation, can coincide in frequency in monkeys and humans. Neuronal mechanisms of selective sensitivity of neural networks to different frequencies of photostimulation are discussed.

The study was aimed at investigating specificities of brain functioning when comparing verbal signals in cross-modal interaction. Event-related potentials (ERPs) were recorded in 166 healthy subjects in a two-modal, two-stimulus test in the GO–NOGO paradigm where the first stimulus was a visual presentation of the word, and the second stimulus was an auditory presentation of the word. It was shown that while the subjects were waiting for the spoken word to follow the printed word, an activation was recorded in the frontal-temporal area of their left hemisphere ( F 7 , F 3 ), presumably associated with the formation of the word’s phonological representation in the memory. Upon producing the second (spoken) word if it coincides with the visual presentation, the peak activation is recorded in the posterior temporal positions ( Т 5 , Т 6 ) within the interval of 370–500 ms; if the second stimulus does not coincide with the first one, the peak activation is recorded in the occipital positions ( O 1 , O 2 ) within the interval of 590–800 ms. These ERP fluctuations supposedly result from the processes of comparing the newly received auditory information with the phonological representation of the word stored in the working memory.

Rhythmic light stimulation can alter the electrical activity of the human and animal brain. Moreover, the brain response to certain flicker frequencies significantly exceeds the responses to neighboring frequencies. This phenomenon is thought to be related with the effect of resonance, as evidenced by the coincidence of one of the maxima in the profile of the response to flashes with the frequency of the alpha rhythm. However, other frequencies that cause an increased response to flashes are not reflected in electroencephalogram (EEG) as dominant oscillations. The goal of this study was to reveal the relationship between local maxima in the profile of the responses to flashes of different frequencies and dominant brain oscillations recorded in electrocorticogram (ECoG) of rhesus monkeys without stimulation. The study was carried out on four male rhesus monkeys Macaca mulatta . In three animals, peak responses were elicited by flickers at 8 and 16 Hz, while one monkey showed the second peak in the 22–30 Hz range. The first maximum (8–10 Hz) in the profile of the response to rhythmic photostimulation coincided with the dominant rhythm recorded in the occipital and parietal regions at rest. The second maximum at 16 Hz coincided with the dominant ECoG rhythm in one of the primates when it was in the state of emotional arousal, which may account for the resonant origin of the increase in responses in this frequency range. The data obtained indicate that dominant brain rhythms, including latent rhythms revealed only by rhythmic photostimulation, can coincide in frequency in monkeys and humans. The mechanisms behind the selective sensitivity of neural networks to different frequencies of photostimulation are discussed.

Infra-low frequency neurofeedback (ILF NF) has been proposed as an alternative or complementary treatment method. Previous studies have reported a good effect of ILF training on the subjective perception of positive psychological changes after training. Here we study whether the objective physiological parameters reflecting the brain function also change under the influence of ILF NF. Eight participants 21–50 years of age with no history of neurological or psychiatric diseases, but reporting about some physiological or psychological complaints, performed 20 sessions of infra-low frequency neurofeedback training. EEG in visual Go/NoGo test was recorded before the course of Neurofeedback and after its completion. The spectral power of slow EEG oscillations in the post-training recording was compared with the pretraining baseline. After 20 sessions of ILF training, the pattern of ILF activity at rest changed dramatically. The main difference was an increase in the amplitude of the ILF activity up to 0.3–1.0 mV in all recording sites. These results indicate that ILF training modified the baseline brain state in each case. Furthermore, after completion of 20 NFB sessions, all participants indicated improvement of their state. Most of them noticed a decrease of inner tension and reactivity to stressful factors. Further, they reported on stability of mood, improved body and space awareness, increase of energy level and of cognitive performance. Along with this remission of the clinical complaints, significant increase of spectral power in 0–0.5 Hz frequency band was observed in all eight participants in the post-training EEG patterns compared to the pretraining EEG. Our study has shown the changes in the amplitude distribution within the ILF spectral range in all participants that seems to be induced by the ILF training. In other words, the ILF training leads to the changes of the functional state of the brain. We suggest that the modification of the baseline ILF EEG pattern may reflect the normalization in the metabolic balance in the brain tissue and increasing efficiency of compensatory mechanisms in the stress regulation systems.

Parallel factor analysis was tested for potential to separate the hidden functionally different components of event-related potentials (ERPs) in a visual cued Go/NoGo task. ERPs were recorded in 351 healthy subjects aged 18–55 years. In the parallel factor analysis model, different components were found to describe a number of well-known ERP waves associated with conflict detection, switching, decision making, and other processes. Based on the waveform analysis of the components, a working hypothesis was proposed to describe the sequence that the processes take to occur in the brain when Go and NoGo stimuli are presented in a Go/NoGo task. Parallel factor analysis thus proved to provide an efficient tool and to allow another viewpoint on brain processes as compared with conventional methods of ERP studies.

Objective: The main goal was to assess common and specific deficits of cognitive control in (Attention Deficit Hyperactivity Disorder) ADHD and schizophrenia (SZ) using event-related potentials (ERPs). Method: Behavioral and EEG data in cued GO/NOGG task were recorded in 132 healthy controls (HC) and age, gender and education matched 63 ADHD adults, and 68 SZ patients. Results: N2d wave in NOGO-GO contrast of ERPs did not differ between the groups while the P3d wave discriminated SZ group from two other groups. Latent components of ERPs were extracted by blind source separation method based on second-order statistics Kropotov et al. (2017) and compared between the groups. A counterpart of N2d wave of a frontally distributed latent component was smaller in SZ indicating a specific frontal dysfunction of conflict detection in SZ. Two centrally distributed P3 subcomponents were reduced in both groups indicating a non-specific dysfunction of action inhibition operations in ADHD and SZ. Conclusion: A pattern of specific and common dysfunctions in terms of latent ERP components shows a more complex picture of functional impairment in schizophrenia and ADHD in comparison to conventional N2/P3 ERP description. Significance: The latent component approach shows a functionally different pattern of cognitive control impairment in comparison to the conventional ERP analysis. (C) 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.