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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2511.07895 |
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| _version_ | 1866909957423628288 |
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| author | Jo, Ha-Na Lee, Jung-Sun Ko, Eunyeong |
| author_facet | Jo, Ha-Na Lee, Jung-Sun Ko, Eunyeong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07895 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Robust EEG-based Intention Decoding during Misarticulated Speech in Dysarthria Jo, Ha-Na Lee, Jung-Sun Ko, Eunyeong Artificial Intelligence 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. |
| title | Toward Robust EEG-based Intention Decoding during Misarticulated Speech in Dysarthria |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.07895 |