Python implementation for text generation using EEG signals from the brain (github.com)

A new Python-based neural decoding pipeline called Thought2Text has been introduced, enabling text generation from brain activity signals through both invasive intracortical speech decoding and non-invasive M/EEG-based typing reconstruction. This modular framework transforms neural signals into text by following a systematic workflow, which includes preprocessing, neural sequence modeling, and applying advanced techniques such as Connectionist Temporal Classification (CTC) and beam search using HuggingFace language models. The pipeline accommodates various data modalities, allowing it to efficiently handle input from different neural signal types. The significance of Thought2Text lies in its potential to advance brain-computer interfaces (BCIs) and neuroprosthetic technologies, making communication accessible for individuals with speech impairments. With built-in synthetic data generators and robust preprocessing utilities, researchers can validate their models without clinical data, expediting advancements in the field. By integrating privacy measures to distinguish between private thought and intended communication, the project also addresses ethical considerations in neural decoding. Overall, Thought2Text represents a meaningful step toward practical applications in brain-to-text communication, highlighting the intersection of AI, healthcare, and neuroscience.