CORSICA：可重复地抑制连续语音诱发的脑电图中人工耳蜗伪迹

文献信息

中文摘要

脑电图（EEG）是研究人工耳蜗（CI）使用者听觉处理的关键工具。特别是，在连续语音过程中获得的EEG记录对于评估CI使用者的言语和语言处理正变得越来越重要，并可能用于神经反馈。然而，CI也会诱发强烈的刺激伪迹，这些伪迹与刺激时间锁定，并掩盖幅度较小的神经反应。现有的伪迹减少方法通常基于事件相关电位（ERP）或需要手动选择成分，使其不适用于自然聆听条件或大型数据集。

我们开发了CORSICA（基于相关性的ICA伪迹拒绝），一种可重复、参数高效的方法，用于减少连续语音EEG反应中的CI伪迹。CORSICA作用于通过Infomax ICA获得的独立成分（IC），不需要手动成分标记，性能由单个可调阈值控制。它利用了以下观察：CI伪迹在时间上跟随音频信号而没有延迟，而神经反应由于听觉通路潜伏期而具有固有滞后。对于每个IC，CORSICA计算与语音刺激的互相关。伪迹通过零滞后附近相关峰的高信噪比（SNR）来识别，如果该SNR超过阈值，则拒绝该成分。为了对CORSICA进行基准测试，我们评估了两种替代方法：一种基于TRF的SNR方法，其中时间响应函数拟合到每个IC，并使用零滞后附近的伪迹驱动峰进行拒绝；以及一种用二阶盲辨识（SOBI）替代ICA作为源分离步骤的变体。

CORSICA有效抑制了CI伪迹，同时保留了神经活动，仅拒绝2%的IC即可恢复生理上合理的TRF。两种基准方法都证实了基于SNR的拒绝框架的有效性，但CORSICA在伪迹抑制质量上优于基于TRF的替代方法。用SOBI替代ICA作为源分离步骤需要拒绝更多的IC，进一步支持ICA作为CORSICA的首选骨干。

CORSICA提供了一种完全客观、无标记的方法来识别语音诱发EEG数据中的CI伪迹，无需手动干预。通过将伪迹拒绝集中在单个可解释的阈值上，它为未来CI使用者言语处理的EEG研究提供了可重复的预处理标准。

我们的发现表明，基于IC的时间反应模式，在语音诱发EEG数据中客观抑制CI伪迹是可行的。

英文摘要

Objective.Electroencephalography (EEG) is a key tool for studying auditory processing in cochlear implant (CI) users. In particular, EEG recordings obtained during continuous speech are becoming increasingly important for assessing speech and language processing in CI users, and may be utilized for neurofeedback. However, CIs also induce strong stimulation artifacts that are time-locked to the stimulus and mask the neural responses that have smaller magnitudes. Existing artifact reduction methods are typically based on event-related potentials or require manual component selection, making them unsuitable for naturalistic listening conditions or large datasets.Approach.We develop CORSICA (CORrelation-baSed ICA artifact rejection), a reproducible, parameter-efficient method for CI artifact reduction in EEG responses to continuous speech. CORSICA operates on independent components (ICs) obtained through Infomax ICA and requires no manual component labeling, with performance governed by a single tunable threshold. It exploits the observation that CI artifacts temporally follow the audio signal without delay, whereas neural responses have an inherent lag due to auditory pathway latencies. For each IC, CORSICA computes the cross-correlation with the speech stimulus. Artifacts are identified by a high signal-to-noise ratio (SNR) of the correlation peak near zero lag, and the component is rejected if this SNR exceeds a threshold. To benchmark CORSICA, we evaluate two alternatives: a TRF-based SNR method, in which temporal response functions are fitted to each IC and artifact-driven peaks near zero lag are used for rejection, and a variant replacing ICA with second-order blind identification (SOBI) as the source separation step.Main results.CORSICA effectively suppressed CI artifacts while preserving neural activity, enabling recovery of physiologically plausible TRFs with only 2% of ICs rejected. Both benchmark methods confirmed the validity of the SNR-based rejection framework, but CORSICA outperformed the TRF-based alternative in artifact suppression quality. Replacing ICA with SOBI as the source separation step required more ICs to be rejected, further supporting ICA as the preferred backbone for CORSICA.Significance.CORSICA provides a fully objective, label-free approach to identifying CI artifacts in speech-evoked EEG data, with no manual intervention required. By centering artifact rejection on a single interpretable threshold, it offers a reproducible preprocessing standard for future EEG studies on speech processing in CI users.Conclusion.Our findings demonstrate that objective CI artifact suppression in speech-evoked EEG data is feasible on the basis of the IC's temporal response patterns.