Igor Kalyakin

Optimal Digital Filtering versus Difference Waves on the Mismatch Negativity in an Uninterrupted Sound Paradigm

Conventionally, mismatch negativity (MMN) is analyzed through the calculation of the difference waves. This helps to eliminate some exogenous event-related potential (ERP) components. However, this reduces the signal-to-noise ratio (SNR). This study aims to test whether or not the optimal digital filtering performs better than the difference waves procedure in quantitative ERP analyses in an uninterrupted sound paradigm. The participants were 102 children aged 8–16 years. The MMN was elicited in a passive oddball paradigm presenting an uninterrupted sound consisting of two alternating tones (600 and 800 Hz) of the same duration (100 msec) with infrequent shortenings of one of the 600 Hz tones (50 or 30 msec). In the grand average, both the 50 and 30 msec tones showed a clear MMN-like activity. Each 100 msec tone elicited some rhythmic activity with relatively consistent ERP waveforms. The difference waves calculated from the offset of the deviant stimuli (time correction due to shortening of the deviant stimuli) failed to separate the MMN from this activity, and produced spurious ERPs at early latencies. The optimal digital filtering freed the MMN from this rhythmic activity, improved the SNR, and thus stabilized the quantitative amplitude and latency analyses of the MMN. The frequency range for optimal extraction of the MMN in this paradigm was 2–8.5 Hz.

Independent component analysis on the mismatch negativity in an uninterrupted sound paradigm

We compared the efficiency of the independent component analysis (ICA) decomposition procedure against the difference wave (DW) and optimal digital filtering (ODF) procedures in the analysis of the mismatch negativity (MMN). The comparison was made in a group of 54 children aged 8-16 years. The MMN was elicited in a passive oddball protocol presenting uninterrupted auditory stimulation consisting of two frequent alternating tones (600 and 800 Hz) of 100 ms duration each. Infrequently, one of the 600 Hz tones was shortened to 50 or 30 ms. The event related potentials (ERPs) were decomposed into the MMN-like and non-MMN-like independent components (ICs) through the FastICA algorithm. The ICA decomposition procedure extracted a cleaner MMN compared to the ODF or DW procedures. It extracted the MMN, whose characteristics concurred with the substantial number of publications demonstrating a significantly larger peak amplitude and shorter latency of the MMN in response to the more deviant stimulus (30 ms) compared to the less deviant stimulus (50 ms). The MMN to these two deviant stimuli did not differ in the peak amplitude or latency when it was extracted through the other two procedures. The ICA decomposition and ODF procedures, similarly, significantly improved the single trial signal-to-noise ratio (SNR) of the MMN compared to the DW procedure. Due to this improvement, the proposed ICA decomposition procedure might allow shortening of the recording session and could be used to study the MMN in paradigms similar to this with small modifications.

SINGLE-TRIAL BASED INDEPENDENT COMPONENT ANALYSIS ON MISMATCH NEGATIVITY IN CHILDREN

Independent component analysis (ICA) does not follow the superposition rule. This motivates us to study a negative event-related potential - mismatch negativity (MMN) estimated by the single-trial based ICA (sICA) and averaged trace based ICA (aICA), respectively. To sICA, an optimal digital filter (ODF) was used to remove low-frequency noise. As a result, this study demonstrates that the performance of the sICA+ODF and aICA could be different. Moreover, MMN under sICA+ODF fits better with the theoretical expectation, i.e., larger deviant elicits larger MMN peak amplitude.

Answering six questions in extracting children's mismatch negativity through combining wavelet decomposition and independent component analysis.

This study combines wavelet decomposition and independent component analysis (ICA) to extract mismatch negativity (MMN) from electroencephalography (EEG) recordings. As MMN is a small event-related potential (ERP), a systematic ICA based approach is designed, exploiting MMN’s temporal, frequency and spatial information. Moreover, this study answers which type of EEG recordings is more appropriate for ICA to extract MMN, what kind of the preprocessing is beneficial for ICA decomposition, which algorithm of ICA can be chosen to decompose EEG recordings under the selected type, how to determine the desired independent component extracted by ICA, how to improve the accuracy of the back projection of the selected independent component in the electrode field, and what can be finally obtained with the application of ICA. Results showed that the proposed method extracted MMN with better properties than those estimated by difference wave only using temporal information or ICA only using spatial information. The better properties mean that the deviant with larger magnitude of deviance to repeated stimuli in the oddball paradigm can elicit MMN with larger peak amplitude and shorter latency. As other ERPs also have the similar information exploited here, the proposed method can be used to study other ERPs.

Analysis on subtracting projection of extracted independent components from EEG recordings.

Abstract Event-related potentials of electroencephalography (EEG) recordings can be assumed as mixtures of sources of electrical brain activities. To reject artifact sources, the projection of the estimated counterpart by independent component analysis (ICA) is often subtracted from EEG recordings. However, the association of performance of ICA decomposition and the subtraction has never been analyzed before. Coincidently, we find that a source can be completely removed from EEG recordings through the subtraction theoretically. The necessary condition of such results is that the estimated ICA model for every source should be entirely correct, that is, each estimated source is just the scaled version of one source. Meanwhile, we also find that the subtraction cannot sufficiently reject one source practically. This is because the estimated ICA model for some sources is inevitably incorrect, that is, some estimated sources are still the mixture of a few sources. To improve the accuracy of the subtraction, it is first necessary to develop better ICA algorithms to separate mixtures as sufficiently as possible and secondly it is necessary to modify the abnormal polarity of the projection of the estimated source in the electrode field. Numerical simulations validate the effectiveness of the modification on the abnormal polarity in rejecting one source.

Extraction of the mismatch negativity elicited by sound duration decrements: A comparison of three procedures

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.

Can back-projection fully resolve polarity indeterminacy of independent component analysis in study of event-related potential?

a b s t r a c t In the study of event-related potentials (ERPs) using independent component analysis (ICA), it is a traditional way to project the extracted ERP component back to electrodes for correcting its scaling (magnitude and polarity) indeterminacy. However, ICA tends to be locally optimized in practice, and then, the back-projection of a component estimated by the ICA can possibly not fully correct its polarity at every electrode. We demonstrate this phenomenon from the view of the theoretical analysis and numerical simulations and suggest checking and modifying the abnormal polarity of the projected component in the electrode field before further analysis. Moreover, when several components are to be projected, instead of the parallel projection of those components simultaneously, the sequential projection of component by component permits the correction of the abnormal polarity of a certain projected component at a certain electrode, which can improve the accuracy of the back-projection. Furthermore, after one extracted component by the ICA is projected back to electrodes under the global optimization, we cannot achieve the real source yet, but the determined scaled source, i.e., the multiplication between the real source and the mapping coefficient from the source to the point at the scalp.

ERP denoising in multichannel EEG data using contrasts between signal and noise subspaces

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.

Extract mismatch negativity and p3a through two-dimensional nonnegative decomposition on time-frequency represented event-related potentials

This study compares the row-wise unfolding nonnegative tensor factorization (NTF) and the standard nonnegative matrix factorization (NMF) in extracting time-frequency represented event-related potentials—mismatch negativity (MMN) and P3a from EEG under the two-dimensional decomposition The criterion to judge performance of NMF and NTF is based on psychology knowledge of MMN and P3a MMN is elicited by an oddball paradigm and may be proportionally modulated by the attention So, participants are usually instructed to ignore the stimuli However the deviant stimulus inevitably attracts some attention of the participant towards the stimuli Thus, P3a often follows MMN As a result, if P3a was larger, it could mean that more attention would be attracted by the deviant stimulus, and then MMN could be enlarged The MMN and P3a extracted by the row-wise unfolding NTF revealed this coupling feature However, through the standard NMF or the raw data, such characteristic was not evidently observed.

Non-negative matrix factorization Vs. FastICA on mismatch negativity of children

In this presentation two event-related potentials, mismatch negativity (MMN) and P3a, are extracted from EEG by non-negative matrix factorization (NMF) simultaneously. Typically MMN recordings show a mixture of MMN, P3a, and responses to repeated standard stimuli. NMF may release the source independence assumption and data length limitations required by Fast independent component analysis (FastICA). Thus, in theory NMF could reach better separation of the responses. In the current experiment MMN was elicited by auditory duration deviations in 102 children. NMF was performed on the time-frequency representation of the raw data to estimate sources. Support to Absence Ratio (SAR) of the MMN component was utilized to evaluate the performance of NMF and FastICA. To the raw data, FastICA-MMN component, and NMF-MMN component, SARs were 31, 34 and 49dB respectively. NMF outperformed FastICA by 15dB. This study also demonstrates that children with reading disability have larger P3a than control children under NMF.