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

Abstract

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.

Validating rationale of group-level component analysis based on estimating number of sources in EEG through model order selection

“…Regarding a LTM, theoretically, we cannot know the mixing matrix through PCA or ICA, but the multiplication of a source and its corresponding mixing coefficients (Cong et al, 2010(Cong et al, , 2011b. Furthermore, an order of extracted components by ICA is inherently indeterminate (Hyvärinen et al, 2001), as a result, it is difficult to straightforwardly validate the rationale of the assumption for group CA regarding the mixing matrix.…”

Analyzing the Dynamics of Emotional Scene Sequence Using Recurrent Neuro-Fuzzy Network

“…12-channel EEG signals are recorded and we adopt wavelet decomposition (WLD) (Burrus et al 1997) for denoising EEG in our study. The selected wavelet filter for denoising the raw EEG signal is the reverse biothorgonal6.8 (rbio6.8) (Mallat 1989;Cong et al 2012), and we select the D7, D8, and D9 to reconstruct the desired ''real signals'' (Zhang and Lee 2012). We focus on the 500 ms time course starting from stimulus onset and extract the power difference between left and right hemispheres in both alpha and gamma band to monitor the valence state of test subjects (Niemic 2002;Müller et al 1999).…”

How to validate similarity in linear transform models of event-related potentials between experimental conditions?

“…For such a paradigm, the signal to noise ratio is relatively low in contrast to the active oddball paradigm to elicit, for example, P300. In this case, it is better to perform ICA on the averaged trace, not the EEG data of concatenated single trials (Cong et al, 2011b). Hence, after the artifacts rejection, all kept trials were averaged to produce the averaged trace at each electrode site and each deviant type separately for each participant.…”

“…Due to only 14 sensors used in our study, the appropriately designed wavelet filter assisted to convert the absolutely underdetermined linear transform model of the ordinary averaged data to the quasi-determined one (Astikainen et al, 2013;Cong et al, 2011e). This is because the used wavelet filter was in light of the properties of the desired ERP and could remove many sources of no interest (Astikainen et al, 2013;Cong et al, 2011b). Hence, it was used here too.…”

“…It has been performed on the EEG data of a single trial (Cong et al, 2010;Iyer and Zouridakis, 2007), the concatenated EEG data of a number of single trials (Delorme and Makeig, 2004;Eichele et al, 2011), and the averaged EEG data over many single trials (Cong et al, 2011b;Makeig et al, 1997). In this study, we focus on the data processing for the averaged EEG data.…”