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Spike sorting plays an irreplaceable role in understanding brain codes.Traditional spike sorting technologies perform feature extraction andclustering separately after spikes are well detected. However, it may oftencause many additional processes and further lead to low-accurate and/orunstable results especially when there are noises and/or overlapping spikes indatasets. To address these issues, in this paper, we proposed a unifiedoptimisation model integrating feature extraction and clustering for spikesorting. Interestingly, instead of the widely used combination strategies,i.e., performing the principal component analysis (PCA) for spike featureextraction and K-means (KM) for clustering in sequence, we unified PCA and KMinto one optimisation model, which reduces additional processes with feweriteration times. Subsequently, by embedding the K-means++ strategy forinitialising and a comparison updating rule in the solving process, theproposed model can well handle the noises and/or overlapping interference.Finally, taking the best of the clustering validity indices into the proposedmodel, we derive an automatic spike sorting method. Plenty of experimentalresults on both synthetic and real-world datasets confirm that our proposedmethod outperforms the related state-of-the-art approaches.

A Unified Model of Feature Extraction and Clustering for Spike Sorting