ArticleScientific reports2018
Spike sorting based on shape, phase, and distribution features, and K-TOPS clustering with validity and error indices.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Article
- Hybrid knowledge- and data-driven modelling for robust spike detection and sorting in human C-fiber microneurography.Scientific reports · 2026Article
- Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction.PLoS biology · 2025Article
- A Study of Deep Clustering in Spike Sorting.Neuroinformatics · 2025Article
- A Study of Non-Linear Manifold Feature Extraction in Spike Sorting.Neuroinformatics · 2025Article
- Real-time, neural signal processing for high-density brain-implantable devices.Bioelectronic medicine · 2025Review
- Article
- Unsupervised spike sorting for multielectrode arrays based on spike shape features and location methods.Biomedical engineering letters · 2024Article
- Article
- Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration After Tetraplegia.Neurosurgery · 2024Article
- Cerebellar interpositus nucleus exhibits time-dependent errors and predictive responses.NPJ science of learning · 2024Article
- Article
- Thermal neuromodulation using pulsed and continuous infrared illumination in a penicillin-induced acute epilepsy model.Scientific reports · 2023Article
- Data Compression Versus Signal Fidelity Tradeoff in Wired-OR Analog-to-Digital Compressive Arrays for Neural Recording.IEEE transactions on biomedical circuits and systems · 2023Article
- Article
- Improved space breakdown method - A robust clustering technique for spike sorting.Frontiers in computational neuroscience · 2023Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
- An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions.Scientific reports · 2021Article
- Article
- Super-Selective Reconstruction of Causal and Direct Connectivity With Application toFrontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spike sorting is one of the most important data analysis problems in neurophysiology. The precision in all steps of the spike-sorting procedure critically affects the accuracy of all subsequent analyses. After data preprocessing and spike detection have been carried out properly, both feature extraction and spike clustering are the most critical subsequent steps of the spike-sorting procedure. The proposed spike sorting approach comprised a new feature extraction method based on shape, phase, and distribution features of each spike (hereinafter SS-SPDF method), which reveal significant information of the neural events under study. In addition, we applied an efficient clustering algorithm based on K-means and template optimization in phase space (hereinafter K-TOPS) that included two integrative clustering measures (validity and error indices) to verify the cohesion-dispersion among spike events during classification and the misclassification of clustering, respectively. The proposed method/algorithm was tested on both simulated data and real neural recordings. The results obtained for these datasets suggest that our spike sorting approach provides an efficient way for sorting both single-unit spikes and overlapping waveforms. By analyzing raw extracellular recordings collected from the rostral-medial prefrontal cortex (rmPFC) of behaving rabbits during classical eyeblink conditioning, we have demonstrated that the present method/algorithm performs better at classifying spikes and neurons and at assessing their modulating properties than other methods currently used in neurophysiology.
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Registered trials
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