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2021-03-28 04:48:49 +02:00
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@@ -6,7 +6,7 @@ The main files are 'preprocessing_and_cleaning.py', 'erp_analysis.py' and 'decod
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 : Hold 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.
- 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.
@@ -15,3 +15,11 @@ The folder 'test' holds mostly unittests that test helper functions and one func
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