A framework for on-implant spike sorting based on salient feature selection

MohammadAli Shaeri and Amir M. Sodagar ()
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MohammadAli Shaeri: York University
Amir M. Sodagar: York University

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract On-implant spike sorting methods employ static feature extraction/selection techniques to minimize the hardware cost. Here we propose a novel framework for real-time spike sorting based on dynamic selection of features. We select salient features that maximize the geometric-mean of between-class distances as well as the associated homogeneity index effectively to best discriminate spikes for classification. Wave-shape classification is performed based on a multi-label window discrimination approach. An external module calculates the salient features and discrimination windows through optimizing a replica of the on-implant operation, and then configures the on-implant spike sorter for real-time online operation. Hardware implementation of the on-implant online spike sorter for 512 channels of concurrent extra-cellular neural signals is reported, with an average classification accuracy of ~88%. Compared with other similar methods, our method shows reduction in classification error by a factor of ~2, and also reduction in the required memory space by a factor of ~5.

Date: 2020
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DOI: 10.1038/s41467-020-17031-9

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