IEEE Trans Neural Syst Rehabil Eng. 2024;32:2854-2863. doi: 10.1109/TNSRE.2024.3438149. Epub 2024 Aug 12.
Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in auditory rehabilitation outcomes following cochlear implantation may be due to cerebral reorganization. Electroencephalography (EEG), favored for its CI compatibility and non-invasiveness, has become a staple in clinical objective assessments of cerebral plasticity post-implantation. However, the electrical activity of CI distorts neural responses, and EEG susceptibility to these artifacts presents significant challenges in obtaining reliable neural responses. Despite the use of various artifact removal techniques in previous studies, the automatic identification and reduction of CI artifacts while minimizing information loss or damage remains a pressing issue in objectively assessing advanced auditory functions in CI recipients. To address this problem, we propose an approach that combines machine learning algorithms-specifically, Support Vector Machines (SVM)-along with Independent Component Analysis (ICA) and Ensemble Empirical Mode Decomposition (EEMD) to automatically detect and minimize electrical artifacts in EEG data. The innovation of this research is the automatic detection of CI artifacts using the temporal properties of EEG signals. By applying EEMD and ICA, we can process and remove the identified CI artifacts from the affected EEG channels, yielding a refined signal. Comparative analysis in the temporal, frequency, and spatial domains suggests that the corrected EEG recordings of CI recipients closely align with those of peers with normal hearing, signifying the restoration of reliable neural responses across the entire scalp while eliminating CI artifacts.
人工耳蜗（CI）是一种神经假体，可以为重度至极重度听力损失的患者恢复听力。观察到人工耳蜗植入后听觉康复结果的可变性可能是由于大脑重组。脑电图（EEG）因其与 CI 的兼容性和非侵入性而成为评估植入后大脑可塑性的临床客观评估的主要方法。然而，CI 的电活动会扭曲神经反应，并且 EEG 对这些伪影的敏感性在获得可靠的神经反应方面带来了重大挑战。尽管在以前的研究中使用了各种去除伪影的技术，但在客观评估 CI 受者的高级听觉功能时，自动识别和减少 CI 伪影，同时最大限度地减少信息丢失或损坏仍然是一个紧迫的问题。为了解决这个问题，我们提出了一种结合机器学习算法（特别是支持向量机（SVM））、独立成分分析（ICA）和集合经验模态分解（EEMD）的方法，以自动检测和最小化 EEG 数据中的电伪影。这项研究的创新之处在于使用 EEG 信号的时间特性自动检测 CI 伪影。通过应用 EEMD 和 ICA，我们可以处理和去除受影响的 EEG 通道中识别出的 CI 伪影，生成更精细的信号。在时间、频率和空间域的比较分析表明，CI 受者的校正 EEG 记录与具有正常听力的同龄人的记录非常吻合，这意味着在消除 CI 伪影的同时，整个头皮上可靠的神经反应得以恢复。
