Code for "MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training"

A brain-computer interface (BCI) for decoding speech phonemes from EEG signals using a hybrid CNN-LSTM model, with interactive Streamlit visualizations and Docker support.

This repostiry contains the code requires to train a SVM and LDA models to distinguish between perceived speech and silence from fNIRS signals.

Post-hoc analysis of relevant spatiotemporal features for speech decoding using a linear support vector classifier and LDA. The post-hoc analysis is made by using the SHAP technique