Brain2Text25
Decoding neural signals into text with a CNN-GRU model
Status not recorded · Dormant
- Problem
- A brain–computer interface produces noisy time-series; the task is to turn that signal into the phonemes a person is trying to say.
- Approach
- A CNN-GRU temporal model with greedy CTC decoding, scored end-to-end with Levenshtein distance.
- Outcome
- An end-to-end signal → phoneme → text pipeline, evaluated on edit distance against ground truth.
PyTorch CTC