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

Abstract

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.

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

“…The temporal information is derived from the experiment paradigm to elicit ERPs, and the spectral information can be achieved through observing the changes of peak amplitudes of ERPs with a group of lowpass and high-pass digital filters (Kalyakin et al 2007), and the spatial information naturally exists in the multi-channel EEG recordings. Since almost every ERP possesses the similar properties exploited here, the proposed method in this study can also be used in the research of other ERPs.…”

“…As introduced by Tie et al (2008), methods used to choose the desired components are usually based on spatial patterns, map polarities, temporal characteristics, spectral criteria, and so on. For example, the MMN peak amplitude is positive in the mastoid and negative at the frontal and central areas with the reference to the tip of nose; MMN elicited by an uninterrupted sound used in this study is time-locked and should appear in the time frame from 50 ms to 200 ms after the deviant offset ); the optimal frequency band of MMN is between 2 Hz and 8.5 Hz in our dataset (Kalyakin et al 2007).…”

“…It should be noted that the MMN peak latency here was the duration from the offset of the deviant to the MMN peak. This is often used to study MMN elicited by the duration deviant (Kalyakin et al 2007;Huttunen-Scott et al 2008).…”

“…To SAR of MMN in this study, the mean value of energy over the rectangular region of the TFR of a component was the support. The dimensions of this rectangle were time by frequency and the frequency range was set as 2-8.5 Hz (Kalyakin et al 2007) and the time interval was between 50 ms and 200 ms after the deviant was offset ). The mean value of energy of the rest region in the TFR of the component was the absence.…”

“…The goal of ICA decomposition is to extract the independent sources from the mixtures through exploiting the spatial independence among different unknown sources (Hyvarinen et al 2001). ICA does not essentially utilize other prior knowledge of sources, such as, temporal or frequency information which is necessary for the digital or wavelet filter (Kalyakin et al 2007;Cong et al 2010a). Using ICA to study electroencephalography (EEG) assumes that EEG collected from any point of the human scalp includes activities generated within a large brain area, and that the spatial smearing of EEG data below 1 kHz by volume conduction does not result in significant time delay (Hämäläinen et al 1993;Makeig et al 1997;Makeig et al 1999;Nunez and Srinivasan 2005).…”