The purpose of this exercise is to learn and get some first-hand experience with analysing neural electrophysiology data. Neural electrophysiology data is used across a wide range of applications for both basic science research as well as commercial and clinical applications. It is extensively used to understand brain functionality in health and disease as well as to develop novel technologies such as brain-computer interfaces. Understanding and implementing basic steps in the processing of such signals will provide you with useful experience that can be applied across a broad range of neural data.
In part 1 of this exercise, you will get some background about the dataset that you will be using and make the necessary preparation for working with this dataset. In part 2, you will identify the behavioural markers that characterise what the participants were doing while their brain activity was recorded. Part 3 will focus on one of the pre-processing steps – re-referencing. In part 4, you will visually explore the data and complete the pre-processing. Part 5 will include the computation of event-related potentials (ERPs) – neural responses that are time-locked to behaviour. Finally, in part 6 you will conduct basic spectral analysis and investigate whether spectral changes are related to behaviour in the dataset.
Note: only parts 1-5 are included in this manual. Part 6 will be added at a later stage following the progress in parts 1-5.
General instructions:
- This exercise will be conducted in Matlab and we expect some prior knowledge in Matlab. This exercise can also be performed in R for those who are not familiar with Matlab. Consult the lab instructor before the lab if you are not familiar with Matlab/R.
- The exercise includes a main script, ‘analysis_bioeng_lab.m’, from which all the functions will be called. Enter your name in the main script in the appropriate place. Other than your name, do not change the main script. It is expected that the code that you will be writing will work with this main script.
Note: the script includes the relevant lines for part 6. Instructions for this part will be added to the manual at a later stage.
- Throughout the exercise, you will see in bold the outputs that you will need to produce and submit:
o Functions – should be written in Matlab (or R)
o Figures – should be generated in Matlab (or R) and saved in both ‘png’ and ‘fig’ (if working with Matlab) formats with the figure number as the file name (e.g., for figure 1 the file name should be ‘Figure1’).
o Questions – answer all the questions in a separate file. Keep the numbering so we know which answer refers to which question. Please also include at the beginning of this document the Matlab/R version that you used for this exercise.
- Additionally, submit the output data file (.mat) that is generated at the end of the main script.
- For all the plots and figures in the exercise – make sure you include labels for the x and y axes as well as legends and titles
- When working with data files (loading/saving), use the ‘fullfile’ function such that the full path will be compatible with different operating systems.
- We included detailed instructions for some of the Matlab functions. If you are unfamiliar with some of the functions, use the Matlab ‘doc’ and/or the web to find out more (we do that all the time!).
Part 1: The electrocorticography (ECOG) signal and the dataset
ECOG is a unique neural signal that is recorded invasively directly from the exposed brain. Its main benefits are both high temporal (sub-millisecond) and high spatial (< 1 cm) resolution that cannot be obtained using non-invasive methods such as electroencephalogram (EEG). ECOG is recorded using especially-designed electrodes, and because of the invasive nature of this technique, it is only used in patients who undergo surgery for clinical purposes. Therefore, access for ECOG data is rather limited.
In this exercise, you will be analysing an ECOG dataset that is publicly available and has been published in the following paper:
Kai J. Miller, A library of human electrocorticographic data and analyses, Nature Human Behavior 2019, 3:1225-1235.
The paper is included in the exercise’s zip file.
Specifically, you will be working with parts of the fingerflex dataset. The relevant parts of the data are included in the exercise’s zip file so there is no need to download them independently.
Download and unzip the exercise’s zip file. It contains the data, relevant documents and relevant papers.
Read the following parts of the paper: introduction (the first paragraph under the ‘Main’ title), Figure 1, Figure 2 and Figure 3.
This reading will provide you with information about the ECOG signal, how it was recorded, and what it measures.
As part of the dataset, the author provided a README file that describes the dataset. The file ‘README_fingerflex_dataset_notes_BioEngLab.docx’ is included in the exercise’s zip file, adjusted for the purpose of this course.
Read this file to learn about the dataset and the task that the participants were doing while their brain activity was recorded.
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