New approaches to eliminating common-noise artifacts in recordings from intracortical microelectrode arrays: Inter-electrode correlation and virtual referencing

Abstract

Intracortical microelectrode arrays record multi-unit extracellular activity for neurophysiology studies and for brain-machine interface applications. The common first step is neural spike detection; a process complicated by common-noise signals from motion artifacts, electromyographic activity, and electric field pickup, especially in awake/behaving subjects. Often common-noise spikes are very similar to neural spikes in their magnitude, spectral, and temporal features. Provided sufficient spacing exists between electrodes of the array, a local neural spike is rarely recorded on multiple electrodes simultaneously. This is not true for distant common-noise sources. Two new techniques compatible with standard spike detection schemes are introduced and evaluated. The first method, virtual referencing (VR), takes the average recording from all functional electrodes in the array (represents the signal from a virtual electrode at the array's center) and subtracts it from the test electrode signal. The second method, inter-electrode correlation (IEC), computes a correlation coefficient between threshold exceeding candidate spike segments on the test electrode and concurrent segments from remaining electrodes. When sufficient correlation is detected, the candidate spike is rejected as originating from a distant common-noise source. The performance of these algorithms was compared with standard thresholding and differential referencing approaches using neural recordings from unanaesthetized rats. By evaluating characteristics of mean-spike waveforms generated by each method under different levels of common-noise, it was found that IEC consistently offered the most robust means of neural spike-detection. Furthermore, IEC's rejection of supra-threshold events not likely originating from local neurons significantly reduces data handling for downstream spike sorting and processing operations.

Bidirectional Control of Generalized Epilepsy Networks via Rapid Real-Time Switching of Firing Mode

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Automatic extracellular spike denoising using wavelet neighbor coefficients and level dependency

“…Unfortunately, it is impossible to avoid false detection or missing detection entirely in spike detection, since there are many noise spikes which are very similar to neural spikes in their magnitude, spectral, and temporal features [23]. An ideal denoising technique would preserve only discriminable spikes without distorting their waveforms.…”

Quality Metrics of Spike Sorting Using Neighborhood Components Analysis

“…In extracellular recordings, spikes recorded on a microelectrode represent a mixture of spikes obtained from one or more neurons and some non-neural spikes, such as noise events from motion artifacts, electromyographic activity, electric field pickup and so on [ 14 ]. In this study, a quantitative metrics based on NCA was proposed for evaluating cluster quality of spikes.…”

“…Note, the aim of NCA is not only to discover new features which could be of interest, but to also use the idea of NCA to estimate the probability that every event is correctly classified, and further to quantify cluster quality. The purpose of using NCA is always the data visualization and fast classification [ 14 ] but here we are discussing it from the cluster quality evaluation perspective.…”