Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals.

ABSTRACT:

Objective

This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training.

Approach

We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes by leveraging their 3D locations on the cortex rather than their p

SUBMITTER: Chen J 

PROVIDER: S-EPMC10980022 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature