Veröffentlichungen

@article{saifurrehman2023adaptive,

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},

doi = {10.48550/arXiv.2304.01355},

year  = {2023},

date = {2023-05-02},

urldate = {2023-05-02},

journal = {arXiv:2304.01355 [cs, math, q-bio]},

keywords = {BCI, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}

@article{10.1088/1741-2552/abc8d4,

title = {SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm},

author = {Muhammad Saif-ur-Rehman and Omair Ali and Susanne Dyck and Robin Lienkämper and Marita Metzler and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},

url = {http://iopscience.iop.org/article/10.1088/1741-2552/abc8d4},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Journal of Neural Engineering},

abstract = {Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called “SpikeDeep-Classifier” is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that “SpikeDeep-Classifier” possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting.},

keywords = {BCI, CNN, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}

@article{Saif-ur-Rehman2019,

title = {SpikeDeeptector: a deep-learning based method for detection of neural spiking activity},

author = {Muhammad Saif-ur-Rehman and Robin Lienkämper and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},

url = {https://iopscience.iop.org/article/10.1088/1741-2552/ab1e63/meta},

doi = {10.1088/1741-2552/ab1e63},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Journal of Neural Engineering},

volume = {16},

number = {5},

pages = {056003},

abstract = {Objective . In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which on...},

keywords = {BCI, CNN, Data Reduction, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}