A New Spike Sorting Algorithm Based on Continuous Wavelet Transform and Investigating Its Effect on Improving Neural Decoding Accuracy.

TL;DR: This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community.

TL;DR: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing, allowing rapid neural spike sorting for multiple neural recording channels.

TL;DR: A comprehensive exploration of unsupervised classification algorithms for spike sorting, encompassing K-means, PCA, SOMs, and hierarchical clustering, with a focus on macaque monkey and human pancreatic signals.

TL;DR: This study proposed a fast and effective spike sorting method (MultiFq) based on multi-frequency composite waveform shapes acquired through an optimized filtering process and demonstrated the compatibility of the method by combining it with other sorting algorithms, which consistently resulted in significant improvement in sorting accuracy.

TL;DR: A new method for detecting and sorting spikes from multiunit recordings that combines the wave let transform with super paramagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions is introduced.

TL;DR: It is demonstrated that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters from the electrical activity of frontoparietal neuronal ensembles.

TL;DR: This work reviews the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms, and proposes a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future.