February 10, 2018

Read e-book online Advanced signal processing on brain event-related potentials PDF

By Cong, Fengyu; Lyytinen, Heikki; Ristaniemi, Tapani

ISBN-10: 9814623083

ISBN-13: 9789814623087

ISBN-10: 9814623091

ISBN-13: 9789814623094

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Read or Download Advanced signal processing on brain event-related potentials : filtering ERPs in time, frequency and space domains sequentially and simultaneously PDF

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Extra resources for Advanced signal processing on brain event-related potentials : filtering ERPs in time, frequency and space domains sequentially and simultaneously

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Freeman, W. J. & Quian Quiroga, R. (2013). Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. New York: Springer. Jung, T. , & Sejnowski, T. J. (2000). Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 111(10), 1745–1758. Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique Cambridge, MA: The MIT Press.

Interestingly, understanding such problem is straightforward using correlation. Equation (2-4) shows that the impulse response of a filter does not change when the sample index t input changes. For the correlation operation, we expect that the two time series to be correlated with each other are stationary; otherwise, the correlation result would not be precise. In Eq. (2-4), the two time series are the impulse response and the sample sequence. 1. In the continuous or concatenated single-trial EEG data, artifacts tend to appear.

Therefore, they can also be applied to filter short ERP/EEG data. As mentioned earlier, these methods are superior over the moving-average-model-based FIR filters. 4. In the next section, we introduce the design of an appropriate wavelet filter. 1 Introduction to wavelet filter The wavelet filter exploits both the temporal and frequency properties of a signal. , 2006; Quian Quiroga & Garcia, 2003; Wilson, 2004). The wavelet filter is usually expressed in terms of the DWT. Therefore, the DWT implementation is the basis for the design of the wavelet filter.

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Advanced signal processing on brain event-related potentials : filtering ERPs in time, frequency and space domains sequentially and simultaneously by Cong, Fengyu; Lyytinen, Heikki; Ristaniemi, Tapani


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