Despiking Neural Data using Linear Interpolation

Excessive spiking in a neural signal can affect the interpretation of lower frequency analyses like those investigating the local field potential (LFP). One method to mitigate the influence of spikes on these low frequency components is to remove them from the original signal using linear interpolation. This technique has been characterized by others and is written into the Fieldtrip toolbox.

Below is a simple function despike.m for MATLAB to remove spikes using linear interpolation.

My primary complaint about this method is that it does not make any “smart” or adaptive decisions concerning the time interval of the spike being replaced. However, if spikes were sorted, each set of spike timestamps from an individual unit could be associated with a spike width (see the spikeWidth variable in my example) and be replaced using a spike-specific time window.

One very simple alternative used when spike contamination is of very high frequency is to simply apply a median filter, like medfilt1. This is less applicable to neural data being sampled at rates greater than ~2kHz where the spike waveform is represented by multiple data points.