Characterization and real-time removal of motion artifacts from EEG signals
Characterization and real-time removal of motion artifacts from EEG signals
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DOI：
10.1088/1741-2552/ab2b61
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发表时间：
2019-10-01
影响因子：
4
通讯作者：
Vidal, Jose Luis Contreras
中科院分区：
文献类型：
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作者：
Kilicarslan, Atilla;Vidal, Jose Luis Contreras
目标。实时非侵入性脑机/计算机接口(BMI/BCI)的准确实现需要处理与测量模式相关的生理性和非生理性伪影。例如，头皮脑电(EEG)测量通常被认为容易产生过多的运动伪影和其他类型的伪影，这些伪影会污染EEG记录。尽管这种伪像的大小在很大程度上取决于任务和设置，但完全最小化或隔离这种伪像通常是不可能的。接近。我们提出了一种具有鲁棒性的自适应去噪框架，使用基于Volterra的非线性映射来表征和处理脑电测量中的运动伪影污染。我们让健康的身体健全的受试者在跑步机上以每小时1到4英里的步速行走，同时我们用基于红外视频的运动跟踪系统跟踪选定脑电电极的运动。我们还在受试者的前额和脚上放置了惯性测量单元(IMO)传感器，以评估整个头部的运动并分割步态。主要结果。我们详细讨论了运动伪影的特点，并提出了一种实时兼容的解决方案来过滤它们。我们报告了对污染的基频(与行走速度同步)及其谐波的有效处理。对步行的事件相关谱摄动(ERSP)分析表明，在所有目标频率上也消除了伪影污染对步态的依赖。意义重大。我们的自适应滤波框架的实时兼容性和通用性允许非侵入性BMI/BCI系统的有效使用，并极大地将实现类型和应用领域扩展到需要信号去噪的其他类型的问题。结合我们以前滤除眼部伪影的工作，提出的技术允许一个全面的自适应滤波框架来提高EEG信噪比(SNR)。我们相信这一实施将使所有非侵入性神经测量方式受益，包括讨论运动和其他内部状态的神经相关性的研究，而不一定是BMI焦点。
Objective. Accurate implementation of real-time non-invasive brain-machine/computer interfaces (BMI/BCI) requires handling physiological and nonphysiological artifacts associated with the measurement modalities. For example, scalp electroencephalographic (EEG) measurements are often considered prone to excessive motion artifacts and other types of artifacts that contaminate the EEG recordings. Although the magnitude of such artifacts heavily depends on the task and the setup, complete minimization or isolation of such artifacts is generally not possible. Approach. We present an adaptive de-noising framework with robustness properties, using a Volterra based non-linear mapping to characterize and handle the motion artifact contamination in EEG measurements. We asked healthy able-bodied subjects to walk on a treadmill at gait speeds of 1-to-4 mph, while we tracked the motion of select EEG electrodes with an infrared video-based motion tracking system. We also placed inertial measurement unit (IMO) sensors on the forehead and feet of the subjects for assessing the overall head movement and segmenting the gait. Main results. We discuss in detail the characteristics of the motion artifacts and propose a real-time compatible solution to filter them. We report the effective handling of both the fundamental frequency of contamination (synchronized to the walking speed) and its harmonics. Event-related spectral perturbation (ERSP) analysis for walking shows that the gait dependency of artifact contamination is also eliminated on all target frequencies. Significance. The real-time compatibility and generalizability of our adaptive filtering framework allows for the effective use of non-invasive BMI/BCI systems and greatly expands the implementation type and application domains to other types of problems where signal denoising is desirable. Combined with our previous efforts of filtering ocular artifacts, the presented technique allows for a comprehensive adaptive filtering framework to increase the EEG signal to noise ratio (SNR). We believe the implementation will benefit all non-invasive neural measurement modalities, including studies discussing neural correlates of movement and other internal states, not necessarily of BMI focus.