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Dimensionality reduction using asynchronous sampling of first derivative features for real-time and computationally efficient neural spike sorting

In recent years, spike sorting has become an emerging technique in multi-channel recording for neuroprosthetic applications. To achieve on-chip real-time processing, it is necessary to design reliable yet low complexity feature extraction and dimensionality reduction to suit low power hardware resources. To satisfy this criterion, this paper proposes asynchronous sampling of first derivative spike waveform features as a dimensionality reduction algorithm. The resulting accuracy of this approach enables identification of the differences temporally localized between the clusters. Directions with maximized or minimized mutual differences are chosen. The classification accuracy of this method is compared with other approaches using different datasets. Using the k-Means clustering algorithm the proposed method achieves an average classification accuracy of > 94% and has very significantly less complexity compared with techniques such as principal component analysis and discrete wavelet transform.