Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces

• Published in: Frontiers in neuroscience 2020-07, Vol.14, p.709-709
• Main Authors: Sadeghi Najafabadi, Mina, Chen, Longtu, Dutta, Kelsey, Norris, Ashley, Feng, Bin, Schnupp, Jan W H, Rosskothen-Kuhl, Nicole, Read, Heather L, Escabí, Monty A
• Format: Article
• Language: English
• Subjects: Algorithms; artifact removal; Axons; brain machine interface; Cochlea; Cochlear implants; Cortex (auditory); electrical stimulation; Electrical stimuli; Electrodes; Interfaces; Mesencephalon; nerve fibers; neural implant; Neuroscience; Noise; Prostheses; Sciatic nerve; Transplants & implants; Vagus nerve
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2020.00709

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, including sciatic 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.