25 lines
2.0 KiB
Markdown
25 lines
2.0 KiB
Markdown
## Semesterproject of the lecture "Semesterproject Signal processing and Analysis of human brain potentials (eeg) WS 2020/21
|
|
|
|
This repository holds the code of the semesterproject as well as the report.
|
|
The main files are 'preprocessing_and_cleaning.py', 'erp_analysis.py' and 'decoding_tf_analyis.py'.
|
|
The files hold:
|
|
- preprocessing_and_cleaning.py : Holds the pre-processing pipeline of the project. By executing the file all subjects are pre-processed. Subjects 001, 003, 014 are pre-processed with manually selected pre-processing information, all other subjects are pre-processed with the given pre-processing information. Pre-processed cleaned data is saved in the BIDS file structure as 'sub-XXX_task-N170_cleaned.fif' where XXX is the subject number.
|
|
Details can be found in the comments of the code.
|
|
- erp_analysis.py : Holds the code for the erp-analysis. Computes the peak-differences and t-tests for several experimental contrasts. Details can be found in the comments of the code.
|
|
- decoding_tf_analysis.py : Holds the code for the decoding and time-frequency analysis. Details can be found in the comments of the code.
|
|
|
|
The folder 'utils' holds helper functions for some plots needed for the analysis and to load data, generate strings etc. and holds the code given in the lecture.
|
|
The folder 'test' holds mostly unittests that test helper functions and one function which visually checks if N170 peaks are extracted correctly.
|
|
|
|
For the code to work properly, the N170 dataset needs to be provided.
|
|
When first running the analysis, it may take a while. After running it one time the data is cached, so that it can be reused if the analysis should be executed again. Be careful though, as a parameter has to be explicitly set in the code, so that the already computed data is used. This parameter is a boolean given to each analysis function which caches data.
|
|
|
|
This code was created using Python 3.7 and the following libraries:
|
|
- Matplotlib 3.3.3
|
|
- MNE 0.22.0
|
|
- MNE-Bids 0.6
|
|
- Numpy 1.19.4
|
|
- Scikit-Learn 0.23.2
|
|
- Pandas 1.2.0
|
|
- Scipy 1.5.4
|