Spencer Kellis1, Larry Sorensen2, Felix Darvas3, Conor Sayres3, Kevin O'Neill4, Richard B Brown5, Paul House6, Jeff Ojemann7, Bradley Greger8. 1. Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA. 2. Department of Physics, University of Washington, Seattle, WA, USA. 3. Department of Neurological Surgery, University of Washington, Seattle, WA, USA. 4. School of Biological & Health Systems Engineering, Arizona State University, Tempe, AZ, USA. 5. Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA; Department of Bioengineering, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA. 6. Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. 7. Department of Neurological Surgery, University of Washington, Seattle, WA, USA; Center for Sensorimotor Neural Engineering, University of Washington, Seattle, WA, USA. 8. School of Biological & Health Systems Engineering, Arizona State University, Tempe, AZ, USA. Electronic address: bradley.greger@asu.edu.
Abstract
OBJECTIVE: Electrocorticography grids have been used to study and diagnose neural pathophysiology for over 50 years, and recently have been used for various neural prosthetic applications. Here we provide evidence that micro-scale electrodes are better suited for studying cortical pathology and function, and for implementing neural prostheses. METHODS: This work compares dynamics in space, time, and frequency of cortical field potentials recorded by three types of electrodes: electrocorticographic (ECoG) electrodes, non-penetrating micro-ECoG (μECoG) electrodes that use microelectrodes and have tighter interelectrode spacing; and penetrating microelectrodes (MEA) that penetrate the cortex to record single- or multiunit activity (SUA or MUA) and local field potentials (LFP). RESULTS: While the finest spatial scales are found in LFPs recorded intracortically, we found that LFP recorded from μECoG electrodes demonstrate scales of linear similarity (i.e., correlation, coherence, and phase) closer to the intracortical electrodes than the clinical ECoG electrodes. CONCLUSIONS: We conclude that LFPs can be recorded intracortically and epicortically at finer scales than clinical ECoG electrodes are capable of capturing. SIGNIFICANCE: Recorded with appropriately scaled electrodes and grids, field potentials expose a more detailed representation of cortical network activity, enabling advanced analyses of cortical pathology and demanding applications such as brain-computer interfaces.
OBJECTIVE: Electrocorticography grids have been used to study and diagnose neural pathophysiology for over 50 years, and recently have been used for various neural prosthetic applications. Here we provide evidence that micro-scale electrodes are better suited for studying cortical pathology and function, and for implementing neural prostheses. METHODS: This work compares dynamics in space, time, and frequency of cortical field potentials recorded by three types of electrodes: electrocorticographic (ECoG) electrodes, non-penetrating micro-ECoG (μECoG) electrodes that use microelectrodes and have tighter interelectrode spacing; and penetrating microelectrodes (MEA) that penetrate the cortex to record single- or multiunit activity (SUA or MUA) and local field potentials (LFP). RESULTS: While the finest spatial scales are found in LFPs recorded intracortically, we found that LFP recorded from μECoG electrodes demonstrate scales of linear similarity (i.e., correlation, coherence, and phase) closer to the intracortical electrodes than the clinical ECoG electrodes. CONCLUSIONS: We conclude that LFPs can be recorded intracortically and epicortically at finer scales than clinical ECoG electrodes are capable of capturing. SIGNIFICANCE: Recorded with appropriately scaled electrodes and grids, field potentials expose a more detailed representation of cortical network activity, enabling advanced analyses of cortical pathology and demanding applications such as brain-computer interfaces.
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