Abstract
The neural recordings in the form of local field potentials offer useful insights on higher-level neural functions by providing information about the activation and deactivation of neural circuits. But often these recordings are contaminated by multiple internal and external sources of noise from nearby electronic systems and body movements. However, to facilitate knowledge extraction from these recordings, identification and removal of the artefacts are empirical, and various computational techniques have been applied for this purpose. Here we report a new module for artefact removal, an extension of the toolbox named SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) which allows for fast application of deep learning techniques to remove said artefacts without relying on data from other channels.
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Fabietti, M., Mahmud, M., Lotfi, A. (2021). A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds) Brain Informatics. BI 2021. Lecture Notes in Computer Science(), vol 12960. Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_32
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