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 realtime 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.