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Data-Driven Multi-Channel Filter Design with Peak-Interference Suppression for Threshold-Based Spike Sorting in High-Density Neural Probes

Spike sorting is the process of assigning each detected neuronal spike in an extracellular recording to its putative source neuron. A linear filter design is proposed where the filter output allows for threshold-based spike sorting of high-density neural probe data. The proposed filter design is based on optimizing the signal-to-peak-interference ratio for each detectable neuron in a data-driven way. Threshold-based spike sorting using linear filters is particularly interesting for real-time spike sorting because of the low computational complexity and predictable delay of those filters, enabling closed-loop neuroscience with unit-activity controlled brain stimulation. We validate our method on both paired and hybrid in-vivo recorded high-density data.