semesterproject_lecture_eeg

This repository holds the code of the semesterproject as well as the report, created by Julius Voggesberger. As the dataset for the project, the N170-dataset was chosen. As the three subjects, to be manually pre-processed, the subjects 001, 003 and 014 were chosen. The rest of the subjects were pre-processed with provided pre-processing information.

Structure

├── Dataset: The dataset of the project as well as the manually selected bad segments are stored here. 
|   ├── n170: Store the dataset here. 
|   └── preprocessed: Bad segments are stored here. 
├── cached_data: Data that is generated in the analysis part is stored here. 
|   ├── decoding_data: Results of the classifiers. 
|   ├── erp_peaks: ERP peaks needed for the ERP analysis. 
|   └── tf_data: Time-frequency data needed for the tf-analysis. 
├── test: Contains unittests and one visual check. 
├── utils: Contains helper methods 
|   ├── ccs_eeg_semesterproject: Methods given in the lecture. 
|   ├── ccs_eeg_utils_reduced: Method for reading in BIDS. 
|   ├── file_utils.py: Methods for reading in files and getting epochs. 
|   └── plot_utils.py: Methods for manually created plots. 
├── preprocessing_and_cleaning.py: The preprocessing pipeline. 
├── erp_analysis.py: The ERP-Analysis and computation of ERP peaks.
└── decoding_tf_analysis.py: Decoding and time-frequency analysis.

Running the project

To run the project python 3.7 is required and anaconda recommended.
To ensure reproducability, randomstates were used for methods which are non-deterministic. The randomstates used are either '123' or '1234'.
The following libraries are needed:

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

For the code to work, the N170 dataset needs to be provided and put into the folder 'Dataset/n170/', so that the file structure 'Dataset/n170/sub-001', etc. exists. The pre-processed raw objects are saved in their respective subject folder, in 'Dataset/n170/'. 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 at a later time. For the cached data to be used, a boolean parameter has to be set in the respective analysis method.

It may be necessary to set the parent directory 'semesterproject_lecture_eeg' as 'Sources Root' for the project, if pycharm is used as an IDE.

Parameters

Parameters have to be changed manually in the code, if different settings want to be tried.

Visualisation

The visualisation methods that were used to generate the visualisations in the report, are contained in the code, if they were created manually. If a visualisation method from mne was used to create the visualisation, it may exist in the code or not.