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

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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.

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Acknowledgments

The first and second authors gratefully thank COMAS, a postgraduate school in computing and mathematical sciences offered by the University of Jyväskylä, Finland, for supporting this study. Cong F is also grateful to professor Amir Averbuch for his invaluable suggestions on wavelet analysis.

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Authors and Affiliations

  1. Department of Mathematical Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland

    Fengyu Cong, Igor Kalyakin & Tapani Ristaniemi

  2. School of Psychology, Beijing Normal University, Beijing, China

    Hong Li

  3. Department of Psychology, University of Jyväskylä, Jyväskylä, Finland

    Tiina Huttunen-Scott & Heikki Lyytinen

  4. NSF I/UCR Center for Intelligent Maintenance System, University of Cincinnati, Cincinnati, OH, USA

    Yixiang Huang

Authors
  1. Fengyu Cong
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  2. Igor Kalyakin
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  3. Hong Li
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  4. Tiina Huttunen-Scott
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  5. Yixiang Huang
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  6. Heikki Lyytinen
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  7. Tapani Ristaniemi
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Correspondence to Fengyu Cong.

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Cong, F., Kalyakin, I., Li, H. et al. Answering six questions in extracting children’s mismatch negativity through combining wavelet decomposition and independent component analysis. Cogn Neurodyn 5, 343–359 (2011). https://doi.org/10.1007/s11571-011-9161-1

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