ArticleBrain informatics2021
SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals.
Article in Brain informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Enhancing neural signal quality: a spike-denoising model with BiLSTM and attention mechanism.Brain informatics · 2025Article
- Article
- ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals.Brain informatics · 2022Article
- Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning.Brain informatics · 2022Article
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Authors and funding
9 authors.
Funding
Abstract
Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.
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Registered trials
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