针对言语障碍者发音错误时的脑电意图解码，提出更鲁棒的分析方法。
Toward Robust EEG-based Intention Decoding during Misarticulated Speech in Dysarthria
- 通过多任务学习抑制非特异性脑电波动
- 在发音错误场景下正确分类F1提升至52.7%
- 适合神经康复与脑机接口研究者参考
发音障碍损害言语运动控制，常导致语义不清和频繁发音错误。尽管脑机接口技术受关注，但针对发音障碍者的脑电（EEG）通信支持仍有限。本研究记录了一名发音障碍患者在韩语自动语音任务中的脑电数据，并将每段试次标记为正确或错误发音。谱分析显示，错误发音试次中额中央区δ波和α波功率升高，颞区γ波活动降低。基于此，我们构建了软多任务学习框架，以抑制非特异性脑电响应，并引入最大均值差异对齐模块，增强类别判别力同时减少域间差异。所提模型在正确试次上达到52.7%的F1分数，错误试次达41.4%，分别比基线提升2%和11%，表明在发音错误条件下仍能实现更稳定的意图解码。结果验证了基于脑电的辅助沟通系统在语言障碍人群中的潜力。
原文摘要 · Abstract (English)
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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