Independent-component-analysis-based spike sorting algorithm for high-density microelectrode array data processing
Ján Šedivý, Urs Frey, David Jäckel, Andreas R. Hierlemann · 2009
Microelectrode arrays (MEAs) become an important tool for neurophysiology research. They are instrumental in revealing neural network formation processes and inter-cell communication schemes, which helps to understand the functioning of the human brain and to treat it's diseases. The electrode pitch of current CMOS-based MEAs can be as low as 18 µm, which allows for recording the activity of single cells simultaneously on several channels [1]. Each electrode in turn records the activity of several adjacent neurons. The presented algorithm employs Independent Component Analysis (ICA) method to recover the spike signals and to assign them to a particular neuron. To overcome the fundamental ICA requirement of linearly mixed independent sources, which is not satisfied in the case of neuronal recordings, the algorithm runs in a loop, successively extracts traces with spiking activity, overlays those with previously detected ones and assigns signals to individual neurons.