Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces
Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces
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DOI：
10.3389/fnins.2020.00709
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发表时间：
2020-07-17
影响因子：
4.3
通讯作者：
Escabi, Monty A.
中科院分区：
文献类型：
--
作者：
Najafabadi, Mina Sadeghi;Chen, Longtu;Escabi, Monty A.
向神经系统提供多部位电刺激的神经植入物不再是最后的手段，而是各种神经系统疾病的常规治疗选择。多部位电刺激也广泛用于研究神经系统功能和神经回路转变。这些技术越来越需要动态电刺激和闭环反馈控制来实时评估神经功能，这在技术上具有挑战性，因为刺激诱发的伪影压倒了感兴趣的小神经信号。我们报告了一种新颖且通用的伪影去除方法，该方法可应用于各种设置，从单到多部位刺激和记录，以及任意形状和大小的电流波形。该方法利用刺激电流和记录伪影之间的线性电耦合，这允许我们估计多通道线性维纳滤波器以预测并随后通过减法去除伪影。我们证实并验证了线性假设，并证明了各种记录方式的可行性，包括在vitrosixiatic神经刺激，双侧人工耳蜗刺激，多通道刺激和记录之间的听觉中脑和皮层。我们展示了一个巨大的增强，在记录质量与典型的伪影减少25-40分贝。该方法是有效的，可以扩展到任意数量的刺激和记录位点，使其成为大规模阵列，闭环植入物和高分辨率多通道脑机接口的理想应用。
Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, includingin vitrosciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25-40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.