/Semantic-Feature-Vector

Exploring the development of universal, task-independent semantic features for EEG signal analysis to enhance understanding and applications in brain-to-brain communication and BCIs.

BSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

Universal Semantic Feature Extraction from EEG Signals

This repository provides a framework for extracting universal, task-independent semantic features from EEG signals. It addresses the limitations of traditional task-specific EEG feature extraction methods by integrating CNNs, AutoEncoders, and Transformers. The resulting high-level semantic representations are robust to inter-subject variability and applicable across diverse EEG paradigms.

We plan to evolve this repository into a more user-friendly, fully automated framework. Users will specify dataset parameters (e.g., sampling rate, channels), and the system will handle preprocessing and feature extraction, streamlining reproducibility and accessibility for the EEG research community.