espresso-neuro 26.5.14

A framework for preprocessing, processing, visualization, event detection, and event curation of high-density time-series signals and multi-channel data streams.

espresso

A Python framework designed for processing high-density electrophysiology signals across multi-channel LFP, EEG, and MEG recording arrays.

Key Capabilities

• Signal Preprocessing: Optimized digital filtering pipelines and artifact rejection workflows.
• Event Detection: Automated extraction of transient oscillations and sharp-wave ripples.
• Signal Visualization: Modern hardware-accelerated time-series traces and interpolated spectrogram displays.
• Event Curation: Interactive manual and automated classification workflows to review, filter, and tag detected transient neural events. (Coming soon)

Installation

pip install espresso-neuro

Usage

Refer to the examples/ directory for complete, runnable pipeline scripts demonstrating signal processing, downsampling metrics, and hardware-accelerated user interface execution.

License & Attribution

This project is licensed under the GNU General Public License v3 - see the LICENSE file for details.

Third-Party Code

• The ripple detection module in src/espresso/ripple_detector/ contains algorithm logic adapted from the FKLab Python Core library by the Kloosterman Lab.