Detection of ERP using Matching Pursuit algorithm
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1
University of West Bohemia, Czechia
Science disciplines like neuroinformatics use modern methods and technologies to help understanding functions of human brain. Arrival of computers into medical laboratories and surgeries can change and upgrade the common way of interpretation of EEG activity. While typical point of interest for neurologists was in time domain of EEG signals, the modern methods are able to interpret records using digital signal processing and automatic classification. In digital signal processing it is often advantageous to analyze a given signal using an adaptive method. One of these methods is the matching pursuit algorithm of Mallat and Zhang. In this method, the given signal can be approximated or represented as a superposition of waveforms that belong to redundant dictionary of functions. The waveforms are selected in order to best match the signal structure. The matching pursuit algorithm is often discussed in the context of discrete Gabor functions on an interval. It is an iterative algorithm decomposing the signal to the set of Gabor functions selected from a pre-defined redundant dictionary. These functions are based on Gaussian window, which is the size changed, shifted and modulated to the following shape(Fig 1): Parameters s, u, v and w indicate scale, translation, frequency and phase of the atom. The optimal approximation is found during decomposition. The best matching waveform selected from dictionary, called atom, is subtracted from the input signal and the whole process is repeated until required accuracy or demanded number of atoms are found. Given the nature of evoked potentials, the matching pursuit algorithm appears as a very suitable form of transformation. The time-frequency identification of the individual atoms makes possible to capture the ERP wave in the signal. This method can be successfully used for detection of the averaged ERP waves. Alternatively, the algorithm can be also used for detection of evoked response in non-averaged signal with aim to minimize ERP detection time.
Conference:
10th International Conference on Cognitive Neuroscience, Bodrum, Türkiye, 1 Sep - 5 Sep, 2008.
Presentation Type:
Poster Presentation
Topic:
Neuroinformatics of Cognition
Citation:
Svoboda
J,
Mautner
P and
Mouček
R
(2008). Detection of ERP using Matching Pursuit algorithm.
Conference Abstract:
10th International Conference on Cognitive Neuroscience.
doi: 10.3389/conf.neuro.09.2009.01.371
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Received:
15 Dec 2008;
Published Online:
15 Dec 2008.
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Correspondence:
Pavel Mautner, University of West Bohemia, Pilsen, Czechia, mautner@kiv.zcu.cz