HFO Artifact

🚀 HFO-Classifier Model Card

This repository offers a collection of machine learning models for classifying high - frequency oscillations (HFOs) in neural signals, which are crucial biomarkers in neurological research.

🚀 Quick Start

The models in this repository are designed to classify high - frequency oscillations (HFOs) in neural signals. HFOs are important biomarkers studied in neurological research, especially in epilepsy and brain function. These models are customized for different classification tasks to assist researchers and clinicians in more effective HFO analysis.

✨ Features

• Comprehensive Classification Pipeline: It covers artifact removal, spkHFO detection, and eHFO detection to streamline HFO analysis workflows.
• State - of - the - Art Models: Built with advanced deep learning techniques to ensure high accuracy and robustness.
• Easy - to - Use API: The models can be directly loaded using Hugging Face's transformers library for seamless integration into research pipelines.

📚 Documentation

Models Included

1. Artifact Detection Model: Identifies and filters out artifacts from HFO signals.
2. spkHFO Detection Model: Detects spike - associated HFOs (spkHFOs), a specific subtype of HFOs associated with epileptic spikes.
3. eHFO Detection Model: Identifies epileptic HFOs (eHFOs), another subtype strongly linked to epileptogenic regions of the brain.

📄 License

This project is licensed under the MIT license.