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When inserting an electrode array in the brain, its electrodes will record so-called ’spikes’ which are generated by the neurons in the neighbourhood of the array. Spike sorting is the process of detecting and assigning these recorded spikes to their putative neurons. Many spike sorting pipelines rely on a clustering algorithm that groups the spikes coming from the same neuron in a pre-defined feature space. However, classical spike sorting algorithms fail when spike overlap, i.e., the near-simultaneous occurrence of two or more spikes from different neurons, is present in the recording. In such cases, the overlapping spikes segment ends up in a seemingly random position in the feature space and is not assigned to the correct cluster. This problem has been addressed before by extending the sorting algorithm with a template matching post-processor. In this work, a novel approach is presented to resolve spike overlap directly in the feature space. To this end, a neural network feature map is presented, that generates spike embeddings (feature vectors) that behave as a linear superposition in the feature space in the case of spike overlap. Its performance is quantified on semi-synthetic data obtained through a data augmentation procedure applied to real neural recordings.

Spike sorting is the process of assigning neural spikes in an extracellular brain recording to their putative neurons. Optimal pre-whitened template matching filters that are used in spike sorting typically suffer from ill-conditioning. In this paper, we investigate the origin of this ill-conditioning and the way in which it influences the resulting filters. Two data-driven subspace regularization approaches are proposed, and those are shown to outperform a regularization approach used in recent literature. The comparison of the methods is based on ground truth data that are recorded in-vivo. ispartof: pages:4376-4379 ispartof: 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) vol:2019 pages:4376-4379 ispartof: 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) location:Berlin, Germany date:23 Jul - 27 Jul 2019 status: published

© 2019 IEEE. An innovative filter design method is proposed for threshold-based spike sorting of high-density neural recordings. Threshold-based spike sorting is the process of assigning each detected spike in an extracellular recording to its putative neuron, using only linear filters and simple thresholding operations. The low computational complexity of threshold-based spike sorting makes it interesting for real-time (hardware) implementations with potential applications in the field of brain-machine interfaces. The proposed method extends our earlier work on discriminative template matching and avoids the need for a prior heuristic definition of an interference covariance matrix. A new optimal filter design objective function is proposed, which automatically selects interference-dominated signal segments based on the output signal of the filter under design. This new method leads to filters that are signal-to-peak-interference ratio (SPIR) optimal. The method is validated on ground truth data recorded in-vivo. ispartof: pages:247-250 ispartof: International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering vol:2019-March pages:247-250 ispartof: 9th International IEEE/EMBS Conference on Neural Engineering (NER) location:San Francisco, USA date:20 Mar - 23 Mar 2019 status: published