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

Ha-Na Jo; Jung-Sun Lee; Eunyeong Ko

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arXiv · 2511.07895

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

Ha-Na Jo·Jung-Sun Lee·Eunyeong Ko

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Abstract

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.

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Ha-Na Jo, Jung-Sun Lee, Eunyeong Ko. 2025-11-11. Toward Robust EEG-based Intention Decoding during Misarticulated Speech in Dysarthria. https://arxiv.org/abs/2511.07895

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