# Toward Robust EEG-based Intention Decoding during Misarticulated Speech in Dysarthria

arXiv: https://arxiv.org/abs/2511.07895 · HTML: https://mlsanity.com/paper/2511.07895/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering](https://mlsanity.com/paper/1711.00108/) (2017)
- [Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signals](https://mlsanity.com/paper/2411.09243/) (2024)
- [EEG Spectral Analysis in Gray Zone Between Healthy and Insomnia](https://mlsanity.com/paper/2411.09875/) (2024)

## Abstract (arXiv)

Dysarthria impairs motor control of speech, often resulting in reduced intelligibility and frequent misarticulations. Although interest in brain-computer interface technologies is growing, electroencephalogram (EEG)-based communication support for individuals with dysarthria remains limited. To address this gap, we recorded EEG data from one participant with dysarthria during a Korean automatic speech task and labeled each trial as correct or misarticulated. Spectral analysis revealed that misarticulated trials exhibited elevated frontal-central delta and alpha power, along with reduced temporal gamma activity. Building on these observations, we developed a soft multitask learning framework designed to suppress these nonspecific spectral responses and incorporated a maximum mean discrepancy-based alignment module to enhance class discrimination while minimizing domain-related variability. The proposed model achieved F1-scores of 52.7 % for correct and 41.4 % for misarticulated trials-an improvement of 2 % and 11 % over the baseline-demonstrating more stable intention decoding even under articulation errors. These results highlight the potential of EEG-based assistive systems for communication in language impaired individuals.