  Objective. Research on brain-computer interfaces (BCIs) is advancing towards
rehabilitating severely disabled patients in the real world. Two key factors
for successful decoding of user intentions are the size of implanted
microelectrode arrays and a good online spike sorting algorithm. A small but
dense microelectrode array with 3072 channels was recently developed for
decoding user intentions. The process of spike sorting determines the spike
activity (SA) of different sources (neurons) from recorded neural data.
Unfortunately, current spike sorting algorithms are unable to handle the
massively increasing amount of data from dense microelectrode arrays, making
spike sorting a fragile component of the online BCI decoding framework.
Approach. We proposed an adaptive and self-organized algorithm for online spike
sorting, named Adaptive SpikeDeep-Classifier (Ada-SpikeDeepClassifier), which
uses SpikeDeeptector for channel selection, an adaptive background activity
rejector (Ada-BAR) for discarding background events, and an adaptive spike
classifier (Ada-Spike classifier) for classifying the SA of different neural
units. Results. Our algorithm outperformed our previously published
SpikeDeep-Classifier and eight other spike sorting algorithms, as evaluated on
a human dataset and a publicly available simulated dataset. Significance. The
proposed algorithm is the first spike sorting algorithm that automatically
learns the abrupt changes in the distribution of noise and SA. It is an
artificial neural network-based algorithm that is well-suited for hardware
implementation on neuromorphic chips that can be used for wearable invasive
BCIs.
