Extraction of mismatch negativity from electroencephalography data
Abstract
In this thesis, we consider three procedures to extract the mismatch negativity, a component of event-related potential, from electroencephalography data: optimal digital filtering, wavelet decomposition, and independent component analysis decomposition procedures. The procedures are compared on two different datasets, stressing their advantages over the conventional difference wave procedure. The main results of the thesis support the use of the wavelet decomposition and independent component analysis decomposition procedures to reveal the experimental effects which are expected from the literature, but not distinguishable through the traditional procedure, and show that these developed procedures may allow us to reduce the duration of an experimental session. Also, we discuss some practical issues related to the use of independent component analysis-based procedures in the extraction of the mismatch negativity. Finally, we consider a method for spatial denoising in multi-channel electroencephalography data, which can be used as a preprocessing step prior to the extraction of the mismatch negativity or any event-related potential as well.
Main Author
Format
Theses
Doctoral thesis
Published
2010
Series
ISBN
978-951-39-9384-9
The permanent address of the publication
https://urn.fi/URN:ISBN:978-951-39-9384-9Käytä tätä linkitykseen.
Language
English
Published in
Jyväskylä studies in computing
Contains publications
- Artikkeli I: Kalyakin, I., González Vega, N., Joutsensalo, J., Huttunen, T., Kaartinen, J., & Lyytinen, H. (2007). Optimal digital filtering versus difference waves on the mismatch negativity in an uninterrupted sound paradigm. Developmental Neuropsychology, 31(3), 429-452. DOI: 10.1080/87565640701229607
- Artikkeli II: Cong, F., Huang, Y., Kalyakin, I., Li, H., Huttunen, T., Lyytinen, H., & Ristaniemi, T. (2012). Frequency-response-based wavelet decomposition for extracting children’s mismatch negativity elicited by uninterrupted sound. Journal of Medical and Biological Engineering, 32(3), 205-214.
- Artikkeli III: Kalyakin, I., González Vega, N., & Lyytinen, H. (2008). Extraction of the Mismatch Negativity on two paradigms using independent component analysis. In 21st IEEE International Symposium on Computer-Based Medical Systems, 59-64. DOI: 10.1109/CBMS.2008.72
- Artikkeli IV: Kalyakin, I., González Vega, N., Kärkkäinen, T., & Lyytinen, H. (2008). Independent component analysis on the mismatch negativity in an uninterrupted sound paradigm. Journal of Neuroscience Methods, 174(2), 301-312. DOI: 10.1016/j.jneumeth.2008.07.012
- Artikkeli V: Kalyakin, I., González Vega, N., Ivannikov, A., & Lyytinen, H. (2009). Extraction of the Mismatch Negativity Elicited by Sound Duration Decrements: A Comparison of Three Procedures. Data & Knowledge Engineering, 68, 1411-1426. DOI: 10.1016/j.datak.2009.07.004
- Artikkeli VI: Cong, F., Kalyakin, I., Ristaniemi, T., & Lyytinen, H. (2008). Drawback of ICA Procedure on EEG: Polarity Indeterminacy at Local Optimization. In 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, 202-205. DOI: 10.1007/978-3-540-69367-3_55
- Artikkeli VII: Cong, F., Zhang, Z., Kalyakin, I., Huttunen-Scott, T., Lyytinen, H., & Ristaniemi, T. (2009). Non-negative matrix factorization vs. FastICA on mismatch negativity of children. In Proceedings of International Joint Conference on Neural Networks, 586-590. DOI: 10.1109/IJCNN.2009.5179068
- Artikkeli VIII: Ivannikov, A., Kalyakin, I., Hämäläinen, J., Leppänen, P. H., Ristaniemi, T., Lyytinen, H., & Kärkkäinen, T. (2009). ERP denoising in multichannel EEG data using contrasts between signal and noise subspaces. Journal of Neuroscience Methods, 180(2), 340-351. DOI: 10.1016/j.jneumeth.2009.03.021
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