Python implementation for text generation using EEG signals from the brain
Thought2Text is a Python-based neural decoding pipeline for converting brain signals into text. It supports both invasive and non-invasive methods for speech decoding and typing reconstruction. The project includes features like multi-modality support, synthetic data generators, and privacy gating for intent classification.
- ▪Thought2Text is designed for both intracortical speech decoding and M/EEG-based typing reconstruction.
- ▪The pipeline includes preprocessing utilities and a CTC-based decoding method for handling variable-length sequences.
- ▪It allows for online, chunked decoding of neural streams and integrates with HuggingFace language models for improved accuracy.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/VanshShah1/Thought2Text |
| Publication time | Mon, 18 May 2026 20:22:14 +0000 |
| Retrieval time | 2026-05-18T20:34:56.987Z |
| Last seen | 2026-05-18T20:34:56.987Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 2e_4GbRikK1L |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Thought2Text Thought2Text is a Python-based neural decoding pipeline designed for reproducing and experimenting with landmark brain-to-text systems. It supports both invasive intracortical speech decoding and non-invasive M/EEG-based typing reconstruction. Overview This project provides a modular framework for transforming neural signals into text. It implements a core workflow common to many state-of-the-art systems: neural signal -> preprocessing -> time-aligned neural features -> neural sequence model -> token probabilities -> beam search + language model -> text Key inspirations include: Willett et al. 2023 (Nature): High-performance speech neuroprosthesis using RNN phoneme decoders with CTC. Kunz et al. 2025 (Cell): Inner speech decoding with motor-intent gating and stack-gated RNNs.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.