This repository contains the codebase for an efficient workflow designed for processing electrophysiological data. The workflow is specifically tailored for analyzing in silico generated data from MEArec.

• Data Processing: The workflow facilitates seamless processing of electrophysiological data originating from MEArec. This includes preprocessing steps to prepare data for further analysis.

• AI-based Spike Detection: The core of this project features a powerful Transformer model customized for spike detection in neurophysiological time series data. This allows advanced analysis and identification of relevant events in the data.

• Evaluation and visualization: Evaluation and visualization functions for further investigation of the development and analyzing process.

• Import: Handling different file formats in order to apply the trained model.

The project was developed under Python 3.9 and Linux Ubuntu 22.04 lts. For machine learning tasks a GPU is recommended.

To use the workflow, follow these steps:

1. Clone the repository: git clone https://github.com/tivenide/SpikeSense
2. Install the required dependencies: pip install -r requirements.txt

Please install MEArec separately and follow their instructions on: https://mearec.readthedocs.io/en/latest/installation.html

Execute data processing with the following command:

python cli/spike_detection.py path/to/MEArec_recording_input_data.h5 path/to/spike_trains_output_data

Only for demonstration purposes to better understand the workflow and check the installation. Please adjust your MEArec data according to your requirement.

1. Use data_generation_MEArec/MEArec_data_generation_quick.py to generate some demo files.
2. Put the recordings for training into quick/MEArec_training_data_recordings.

Execute development (quick) with the following command:

python cli/quick_cli.py develop

Execute data processing with the following command:

python  cli/quick_cli.py process path/to/MEArec_recording_input_data.h5 path/to/spike_trains_output_data

• choosing license

Copyright 2023 tivenide. All rights reserved.