MEA Network Analysis Project

This project analyzes MEA (Multi-Electrode Array) recordings to extract, process, and visualize network properties from neural data. The workflow includes extracting data from H5 files (writed by: OraWetzler), building graphs, drawing visualizations, saving graph metrics, and performing analysis.

Folder Structure

Project/
├── src/
│   ├── __init__.py
│   ├── Analysis.py
│   ├── data.py
│   ├── ElectrodeFeatureExtract.py
│   ├── ExtractH5Files.py
│   ├── GraphBuilder.py
│   ├── GraphDrawer.py
│   ├── GraphMetrics.py
│   ├── GraphMetricsSaver.py
│   ├── HelperServices.py
│   ├── MEAFeatureExtract.py
│   ├── SpikesData.py
├── MEA recordings/
├── graphs/
├── output/
├── analysis_out/
└── ...

Getting Started

1. Clone the Repository

git clone https://github.com/MeitarTeper/Practikum_project.git
cd Practikum_project

2. Create and Activate a Virtual Environment (Recommended)

python -m venv .venv
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

If requirements.txt is missing, install the main dependencies manually:

pip install numpy pandas networkx matplotlib McsPy

Usage Flow

Run the following scripts in order:

1. ExtractH5Files.py
   Extracts data from MEA H5 recordings and saves as CSV or other formats.

   python src/ExtractH5Files.py

2. data.py
   Processes the extracted data for further analysis.

   python src/data.py

3. GraphBuilder.py
   Builds network graphs from the processed data.

   python src/GraphBuilder.py

4. GraphDrawer.py
   Draws and saves visualizations of the graphs.

   python src/GraphDrawer.py

5. GraphMetricsSaver.py
   Calculates and saves graph metrics.

   python src/GraphMetricsSaver.py

6. Analysis.py
   Performs final analysis and generates summary outputs.

   python src/Analysis.py

Notes

• Place your zip files in the main folder (not unser src) before starting.
• Output files and plots will be saved in the MEA recordings, output/, graphs/, and analysis_out/ directories.
• Adjust script parameters as needed for your dataset.