Bayesian estimation of group event-related potential components (BEGEP): testing a model for synthetic and real datasets.

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.

Second Order Blind Identification of Event Related Potentials Sources.

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.

Specificity of spontaneous EEG associated with different levels of cognitive and communicative dysfunctions in children.

This study aimed to reveal electrophysiological markers of communicative and cognitive dysfunctions of different severity in children with autism spectrum disorder (ASD). Eyes-opened electroencephalograms (EEGs) of 42 children with ASD, divided into two groups according to the severity of their communicative and cognitive dysfunctions (24 with severe and 18 children with less severe ASD), and 70 age-matched controls aged 4-9 years were examined by means of spectral and group independent component (gIC) analyses. A predominance of theta and beta EEG activity in both groups of children with ASD compared to the activity in the control group was found in the global gIC together with a predominance of beta EEG activity in the right occipital region. The quantity of local gICs with enhanced slow and high-frequency EEG activity (within the frontal, temporal, and parietal cortex areas) in children 4-9 years of age might be considered a marker of cognitive and communicative dysfunction severity.

ERPs correlates of EEG relative beta training in ADHD children.

Eighty-six children (ages 9-14) with attention deficit hyperactivity disorder (ADHD) participated in this study. Event-related potentials (ERPs) were recorded in auditory GO/NOGO task before and after 15-22 sessions of EEG biofeedback. Each session consisted of 20 min of enhancing the ratio of the EEG power in 15-18 Hz band to the EEG power in the rest of spectrum, and 7-10 min of enhancing of the ratio of the EEG power in 12-15 Hz to the EEG power in the rest of spectrum with C3-Fz electrodes' placements for the first protocol and C4-Pz for the second protocol. On the basis of quality of performance during training sessions, the patients were divided into two groups: good performers and bad performers. ERPs of good performers to GO and NOGO cues gained positive components evoked within 180-420 ms latency. At the same time, no statistically significant differences between pre- and post-training ERPs were observed for bad performers. The ERP differences between post- and pretreatment conditions for good performers were distributed over frontal-central areas and appear to reflect an activation of frontal cortical areas associated with beta training.