Veröffentlichungen

@article{saif-ur-rehmanAdaptiveSpikeDeepclassifierSelforganizing2025,

title = {Adaptive SpikeDeep-classifier: Self-organizing and Self-Supervised Machine Learning Algorithm for Online Spike Sorting},

author = {Muhammad Saif-ur-Rehman and Omair Ali and Christian Klaes and Ioannis Iossifidis},

editor = {Elsevier},

url = {https://www.sciencedirect.com/science/article/pii/S0925231225020429},

doi = {10.1016/j.neucom.2025.131370},

issn = {0925-2312},

year  = {2025},

date = {2025-09-04},

urldate = {2025-09-04},

journal = {Neurocomputing},

pages = {131370},

abstract = {Objective. Invasive brain-computer interface (BCI) research is progressing towards the realization of the motor skills rehabilitation of severely disabled patients in the real world. The size of invasive micro-electrode arrays and the selection of an efficient online spike sorting algorithm (performing spike sorting at run time) are two key factors that play pivotal roles in the successful decoding of the user’s intentions. The process of spike sorting includes the selection of channels that record the spike activity (SA) and determines the SA of different sources (neurons), on selected channels individually. The neural data recorded with dense micro-electrode arrays is time-varying and often contaminated with non-stationary noise. Unfortunately, current state-of-the-art spike sorting algorithms are incapable of handling the massively increasing amount of time-varying data resulting from the dense microelectrode arrays, which makes the spike sorting one of the fragile components of the online BCI decoding framework. Approach. This study proposed an adaptive and self-organized algorithm for online spike sorting, named as Adaptive SpikeDeep-Classifier (Ada-SpikeDeepClassifier). Our algorithm uses SpikeDeeptector for the channel selection, an adaptive background activity rejector (Ada-BAR) for discarding the background events, and an adaptive spike deep-classifier (Ada-SpikeDeepClassifier) for classifying the SA of different neural units. The process of spike sorting is accomplished by concatenating SpikeDeeptector, Ada-BAR and Ada-SpikeDeepclassifier. Results. The proposed algorithm is evaluated on two different categories of data: a human data-set recorded in our lab, and a publicly available simulated data-set to avoid subjective biases and labeling errors. The proposed Ada-SpikeDeepClassifier outperformed our previously published SpikeDeep-Classifier and eight conventional spike sorting algorithms and produce comparable results to state of the art deep learning based algorithms. Significance. To the best of our knowledge, the proposed algorithm is the first spike sorting algorithm that autonomously adapts to the shift in the distribution of noise and SA data and perform spike sorting without human interventions in various kinds of experimental settings. In addition, the proposed algorithm builds upon artificial neural networks, which makes it an ideal candidate for being embedded on neuromorphic chips that are also suitable for wearable invasive BCI.},

keywords = {BCI, Brain computer interface, Deep learning, Self organizing, Self-supervised machine learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}