# EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models

arXiv: https://arxiv.org/abs/2411.09302 · HTML: https://mlsanity.com/paper/2411.09302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 1 of 1)

- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)

## Abstract (arXiv)

In this study, we propose an ensemble learning framework for electroencephalogram-based overt speech classification, leveraging denoising diffusion probabilistic models with varying convolutional kernel sizes. The ensemble comprises three models with kernel sizes of 51, 101, and 201, effectively capturing multi-scale temporal features inherent in signals. This approach improves the robustness and accuracy of speech decoding by accommodating the rich temporal complexity of neural signals. The ensemble models work in conjunction with conditional autoencoders that refine the reconstructed signals and maximize the useful information for downstream classification tasks. The results indicate that the proposed ensemble-based approach significantly outperforms individual models and existing state-of-the-art techniques. These findings demonstrate the potential of ensemble methods in advancing brain signal decoding, offering new possibilities for non-verbal communication applications, particularly in brain-computer interface systems aimed at aiding individuals with speech impairments.