@article {48220,
	title = {Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction.},
	journal = {bioRxiv},
	year = {2017},
	month = {2017 Jan 19},
	abstract = {<p>Electrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating a channel-covariance feature and a clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes a 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.</p>
},
	doi = {10.1101/101030},
	author = {Jun, James Jaeyoon and Mitelut, Catalin and Lai, Chongxi and Gratiy, Sergey and Anastassiou, Costas and Harris, Timothy D}
}
