Search

Abstract: Objective: To design a fast algorithm that evaluates the degree of correlation of recordings among single trials (CoRaST) for mismatch negativity (MMN) activity. Methods: The participants were 114 children, aged 8–16years. MMNs were elicited by two deviants in duration that occurred in an uninterrupted sound within a passive oddball paradigm, and each trial lasted 650ms with 130 samples. CoRaST was derived from the frequency-domain MMN model through Fourier transformation. To validate the effectiveness of the proposed method, the wavelet transformation-based inter-trial coherence (ITC) was taken as a reference. Results: Performances of the proposed CoRaST and ITC were similar in evaluating the correlation of MMN activity among single trials. However, the analysis of electroencephalograph (EEG) recordings comprising approximately 330 trials at one channel took approximately 0.12s with CoRaST, whereas ITC required approximately 45s in our workstation. Conclusions: CoRaST has the potential to evaluate the correlation of MMN activity among single trials in a real-time system. Furthermore, the new method can be facilitated to study other event-related potentials (ERPs) of evoked or induced brain activity. Significance: The correlation of ERP activity among single trials can be immediately inspected during the ERP data collection in a real-time system. [Copyright &y& Elsevier]

Abstract: This study addresses how to validate the rationale of group component analysis (CA) for blind source separation through estimating the number of sources in each individual EEG dataset via model order selection. Control children, typically reading children with risk for reading disability (RD), and children with RD participated in the experiment. Passive oddball paradigm was used for eliciting mismatch negativity during EEG data collection. Data were cleaned by two digital filters with pass bands of 1–30Hz and 1–15Hz and a wavelet filter with the pass band narrower than 1–12Hz. Three model order selection methods were used to estimate the number of sources in each filtered EEG dataset. Under the filter with the pass band of 1–30Hz, the numbers of sources were very similar among different individual EEG datasets and the group ICA would be suggested; regarding the other two filters with much narrower pass bands, the numbers of sources were relatively diverse, and then, applying group ICA would not be appropriate. Hence, before group ICA is performed, its rationale can be logically validated by the estimated number of sources in EEG data through model order selection. [Copyright &y& Elsevier]

Abstract: This study focuses on comparison of procedures for extracting the brain event-related potentials (ERPs) – brain responses to stimuli recorded using electroencephalography (EEG). These responses are used to study how the synchronization of brain electrical responses is associated with cognition such as how the brain detects changes in the auditory world. One such event-related response to auditory change is called mismatch negativity (MMN). It is typically observed by computing a difference wave between ERPs elicited by a frequently repeated sound and ERPs elicited by an infrequently occurring sound which differs from the repeated sounds. Fast and reliable extraction of the ERPs, such as the genuine MMN, is an important focus of studies devoted to basic cognitive brain research. In this study, we compared three procedures for extraction of the MMN elicited by infrequent duration decrements in auditory sound stimulation. These were the conventional difference wave (DW) with average standard sweep, optimal digital filtering (ODF), and recently proposed independent component analysis (ICA) decomposition procedures. The statistical comparison was made in a group of 12 healthy adults aged 23–30 years. The MMN was elicited in a passive oddball protocol presenting an auditory stimulation consisting of frequent tones of 600Hz of 100ms duration each (standard stimuli). Infrequently, one of the tones was shortened to 75, 50, or 30ms (deviant stimuli). The ICA decomposition procedure, similarly to the DW procedure with average standard sweep, extracted a cleaner MMN compared to the ODF procedure. Both procedures extracted the MMN, whose amplitude and latency characteristics concur with substantial number of publications in contrast to the ODF procedure. [Copyright &y& Elsevier]

Abstract: In this paper, a new method intended for ERP denoising in multichannel EEG data is discussed. The denoising is done by separating ERP/noise subspaces in multidimensional EEG data by a linear transformation and the following dimension reduction by ignoring noise components during inverse transformation. The separation matrix is found based on the assumption that ERP sources are deterministic for all repetitions of the same type of stimulus within the experiment, while the other noise sources do not obey the determinancy property. A detailed derivation of the technique is given together with the analysis of the results of its application to a real high-density EEG data set. The interpretation of the results and the performance of the proposed method under conditions, when the basic assumptions are violated – e.g. the problem is underdetermined – are also discussed. Moreover, we study how the factors of the number of channels and trials used by the method influence the effectiveness of ERP/noise subspaces separation. In addition, we explore also the impact of different data resampling strategies on the performance of the considered algorithm. The results can help in determining the optimal parameters of the equipment/methods used to elicit and reliably estimate ERPs. [Copyright &y& Elsevier]