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README.md
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README.md
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## Semesterproject of the lecture "Semesterproject Signal processing and Analysis of human brain potentials (eeg) WS 2020/21
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## Semesterproject of the lecture "Semesterproject Signal processing and Analysis of human brain potentials (eeg)" WS 2020/21
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This repository holds the code of the semesterproject as well as the report.
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The main files are 'preprocessing_and_cleaning.py', 'erp_analysis.py' and 'decoding_tf_analyis.py'.
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The files hold:
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- 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.
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Details can be found in the comments of the code.
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- 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.
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- decoding_tf_analysis.py : Holds the code for the decoding and time-frequency analysis. Details can be found in the comments of the code.
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This repository holds the code of the semesterproject as well as the report, created by Julius Voggesberger.
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As the dataset for the project, the N170-dataset was chosen.
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As the three subjects, to be manually pre-processed, the subjects 001, 003 and 014 were chosen.
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The rest of the subjects were pre-processed with provided pre-processing information.
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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.
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The folder 'test' holds mostly unittests that test helper functions and one function which visually checks if N170 peaks are extracted correctly.
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### Structure
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```
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├── Dataset: The dataset of the project as well as the manually selected bad segments are stored here.
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| ├── n170: Store the dataset here.
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| └── preprocessed: Bad segments are stored here.
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├── cached_data: Data that is generated in the analysis part is stored here.
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| ├── decoding_data: Results of the classifiers.
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| ├── erp_peaks: ERP peaks needed for the ERP analysis.
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| └── tf_data: Time-frequency data needed for the tf-analysis.
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├── test: Contains unittests and one visual check.
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├── utils: Contains helper methods
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| ├── ccs_eeg_semesterproject: Methods given in the lecture.
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| ├── ccs_eeg_utils_reduced: Method for reading in BIDS.
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| ├── file_utils.py: Methods for reading in files and getting epochs.
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| └── plot_utils.py: Methods for manually created plots.
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├── preprocessing_and_cleaning.py: The preprocessing pipeline.
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├── erp_analysis.py: The ERP-Analysis and computation of ERP peaks.
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└── decoding_tf_analysis.py: Decoding and time-frequency analysis.
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```
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For the code to work properly, the N170 dataset needs to be provided.
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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.
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This code was created using Python 3.7 and the following libraries:
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### Running the project
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To run the project python 3.7 is required and anaconda recommended.\
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To ensure reproducability, randomstates were used for methods which are non-deterministic.
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The randomstates used are either '123' or '1234'.\
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The following libraries are needed:
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- Matplotlib 3.3.3
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- MNE 0.22.0
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- MNE-Bids 0.6
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@@ -22,3 +37,18 @@ This code was created using Python 3.7 and the following libraries:
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- Scikit-Learn 0.23.2
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- Pandas 1.2.0
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- Scipy 1.5.4
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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.
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The pre-processed raw objects are saved in their respective subject folder, in 'Dataset/n170/'.
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When first running the analysis, it may take a while.
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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.
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For the cached data to be used, a boolean parameter has to be set in the respective analysis method.
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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.
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### Parameters
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Parameters have to be changed manually in the code, if different settings want to be tried.
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### Visualisation
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The visualisation methods that were used to generate the visualisations in the report, are contained in the code, if they were created manually.
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If a visualisation method from mne was used to create the visualisation, it may exist in the code or not.
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@@ -254,5 +254,6 @@ def time_frequency(dataset, filename, scaling='lin', compute_tfr=True):
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if __name__ == '__main__':
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mne.set_log_level(verbose=VERBOSE_LEVEL)
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ds = 'N170'
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decoding(ds, 'faces_vs_cars', True)
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time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', 'log', False)
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# decoding(ds, 'faces_vs_cars', True)
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time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', 'log', True)
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