Abstract
This chapter reviews state-of-the-art observer models of the P300 event-related potential and introduces a new digital filtering (DIF) model. It starts with a brief overview of the models known from literature and of the approach proposed in this work. After a description of the employed variant of the oddball task and specific methods for capturing trial-by-trial fluctuations in the P300 amplitudes y(n), the models and response functions constituting the model space \(\mathcal {M}\) are presented in detail and the two most renowned ones are integrated into the digital filtering model. Next, the parameter optimization schemes as well as the composition of the design matrices for model estimation and selection (see Chap. 2) are specified. Results and conclusions complete this chapter.
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Note that the number of occurrences of all events \(k \in \mathcal {K}\) until trial \(n - 1\) is simply \(\sum _{k\in \mathcal {K}} \tilde{c}_{\mathrm {L},k}(n) = n - 1\).
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© 2016 Springer International Publishing Switzerland
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Kolossa, A. (2016). A New Theory of Trial-by-Trial P300 Amplitude Fluctuations. In: Computational Modeling of Neural Activities for Statistical Inference . Springer, Cham. https://doi.org/10.1007/978-3-319-32285-8_3
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DOI: https://doi.org/10.1007/978-3-319-32285-8_3
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32284-1
Online ISBN: 978-3-319-32285-8
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