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@@ -6,22 +6,22 @@ As the three subjects, to be manually pre-processed, the subjects 001, 003 and 0
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.
├── 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.