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.

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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.

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.

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.

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