diff --git a/README.md b/README.md
index 20f382d..7b7aed6 100644
--- a/README.md
+++ b/README.md
@@ -1,20 +1,31 @@
 ## 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.
+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.
 
-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.
+### 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.
 
-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:
+### Running the project
+To run the project python 3.7 is required and anaconda recommended.
+The following libraries are needed:
 - Matplotlib 3.3.3
 - MNE 0.22.0
 - MNE-Bids 0.6
@@ -23,5 +34,17 @@ This code was created using Python 3.7 and the following libraries:
 - Pandas 1.2.0
 - Scipy 1.5.4
 
-It is recommended to use Anaconda.
-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.
\ No newline at end of file
+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.
\ No newline at end of file