Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning
| Autor: | David J. Caldwell, J. N. Kutz, Andrew L. Ko, Bingni W. Brunton, Jeffrey G. Ojemann, Jeneva A. Cronin, Kelly L. Collins, Rajesh P. N. Rao, Kurt E. Weaver |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Deep brain stimulation
Computer science Deep Brain Stimulation medicine.medical_treatment 0206 medical engineering Biomedical Engineering Stimulation 02 engineering and technology Somatosensory system stimulation Article 03 medical and health sciences Cellular and Molecular Neuroscience 0302 clinical medicine medicine Humans neural signals Artifact (error) Motor Cortex artifact Brain Neural engineering Human brain electrophysiology 020601 biomedical engineering Electric Stimulation Electrophysiology medicine.anatomical_structure Electrocorticography Artifacts Neuroscience 030217 neurology & neurosurgery Motor cortex |
| Zdroj: | Journal of neural engineering |
| ISSN: | 1741-2552 |
| DOI: | 10.1088/1741-2552/ab7a4f |
| Popis: | Objective. Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available. Approach. We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings. Main results. We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation. Significance. Our work will enable future advances in neural engineering with simultaneous stimulation and recording. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|