Abstract
OBJECTIVE: Removal of common mode noise and artifacts from extracellularly measured action potentials, herein referred to as spikes, recorded with multi-electrode arrays (MEAs) which included severe noise and artifacts generated by an ultrahigh field (UHF) 16.4 Tesla magnetic resonance imaging (MRI) scanner. APPROACH: An adaptive virtual referencing (AVR) algorithm is used to remove artifacts and thus enable extraction of neural spike signals from extracellular recordings in anesthetized rat brains. A 16-channel MEA with 150-micron inter-site spacing is used, and a virtual reference is created by spatially averaging the 16 signal channels which results in a reference signal without extracellular spiking activity while preserving common mode noise and artifacts. This virtual reference signal is then used as the input to an adaptive FIR filter which optimally scales and time-shifts the reference to each specific electrode site to remove the artifacts and noise. MAIN RESULTS: By removing artifacts and reducing noise, the neural spikes at each electrode site can be well extracted, even from data originally recorded with a high noise floor due to electromagnetic interference and artifacts generated by a 16.4T MRI scanner. The AVR method enables many more spikes to be detected than would otherwise be possible. Further, the filtered spike waveforms can be well separated from each other using PCA feature extraction and semi-supervised k-means clustering. While data in a 16.4T MRI scanner contains significantly more noise and artifacts, the developed AVR method enables similar data quality to be extracted as recorded on benchtop experiments outside the MRI scanner. SIGNIFICANCE: AVR of extracellular spike signals recorded with MEAs has not been previously reported and fills a technical need by enabling low-noise extracellular spike extraction in noisy and challenging environments such as UHF MRI that will enable further study of neuro-vascular coupling at UHF.