IEEE Trans Neural Syst Rehabil Eng. 2020 Aug;28(8):1825-1835. doi: 10.1109/TNSRE.2020.3000971. Epub 2020 Jun 9.
Motion and muscle artifacts can undermine signal quality in electroencephalography (EEG) recordings during locomotion. We evaluated approaches for recovering ground-truth artificial brain signals from noisy EEG recordings. We built an electrical head phantom that broadcast four brain and four muscle sources. Head movements were generated by a robotic motion platform. We recorded 128-channel dual layer EEG and 8-channel neck electromyography (EMG) from the head phantom during motion. We evaluated ground-truth electrocortical source signal recovery from artifact contaminated data using Independent Component Analysis (ICA) to determine: (1) the number of isolated noise sensor recordings needed to capture and remove motion artifacts, (2) the ability of Artifact Subspace Reconstruction to remove motion and muscle artifacts at contrasting artifact detection thresholds, (3) the number of neck EMG sensor recordings needed to capture and remove muscle artifacts, and (4) the ability of Canonical Correlation Analysis to remove muscle artifacts. We also evaluated source signal recovery by combining the best practices identified in aims 1-4. By including isolated noise and EMG recordings in the ICA decomposition, we more effectively recovered ground-truth artificial brain signals. A reduced subset of 32-noise and 6-EMG channels showed equivalent performance compared to including the complete arrays. Artifact Subspace Reconstruction improved source separation, but this was contingent on muscle activity amplitude. Canonical Correlation Analysis also improved source separation. Merging noise and EMG recordings into the ICA decomposition, with Artifact Subspace Reconstruction and Canonical Correlation Analysis preprocessing, improved source signal recovery. This study expands on previous head phantom experiments by including neck muscle source activity and evaluating artificial electrocortical spectral power fluctuations synchronized with gait events.
运动和肌肉伪影会降低运动时脑电图（EEG）记录的信号质量。我们评估了从噪声 EEG 记录中恢复真实人工脑信号的方法。我们构建了一个电气头模型，该模型广播了四个脑和四个肌肉源。头部运动由机器人运动平台产生。我们在运动过程中从头部模型记录了 128 通道双层 EEG 和 8 通道颈部肌电图（EMG）。我们使用独立成分分析（ICA）评估了从受伪影污染的数据中恢复真实电皮质源信号的方法，以确定：（1）捕获和去除运动伪影所需的隔离噪声传感器记录的数量；（2）在不同的伪影检测阈值下，Artifact Subspace Reconstruction 去除运动和肌肉伪影的能力；（3）捕获和去除肌肉伪影所需的颈部 EMG 传感器记录的数量；（4）Canonical Correlation Analysis 去除肌肉伪影的能力。我们还通过结合前四个目标中的最佳实践来评估源信号恢复。通过在 ICA 分解中包含隔离噪声和 EMG 记录，我们更有效地恢复了真实的人工脑信号。与包括完整阵列相比，减少了 32 个噪声和 6 个 EMG 通道的子集显示出等效的性能。Artifact Subspace Reconstruction 提高了源分离，但这取决于肌肉活动幅度。Canonical Correlation Analysis 也提高了源分离。将噪声和 EMG 记录合并到 ICA 分解中，使用 Artifact Subspace Reconstruction 和 Canonical Correlation Analysis 预处理，可改善源信号恢复。本研究通过包括颈部肌肉源活动，并评估与步态事件同步的人工电皮质光谱功率波动，扩展了以前的头模型实验。
