4.1: Data for this Chapter

Last updated
Save as PDF

Page ID: 87947

\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)\(\newcommand{\AA}{\unicode[.8,0]{x212B}}\)

The data we’ll be using for the exercises in this chapter can be found in the Chapter_4 folder in the master folder: https://doi.org/10.18115/D50056.

All of the exercises in this chapter involve artificial data rather than real EEG or ERP signals. This is because we don’t know the true waveform with real data. With real data, the waveform consists of the sum of an unknown ERP waveform and unknown noise, so when you apply a filter, you don’t know what the result should look like if the filter is working properly. With artificial data, we can create a true waveform and add known noise to it. We can then see how well we can recover the true waveform by filtering the data. In other words, artificial waveforms give us ground truth.

Once you understand how filters work, they’re pretty easy to implement using ERPLAB. You’ve already seen how to filter both EEG and ERP data in the previous chapters, so this chapter will focus on helping you understand how filters work rather than applying them to real data. All of the exercises use ERPsets rather than EEG datasets, but the general principles are the same for EEG and averaged ERPs.