Effectiveness of Employing Multimodal Signals in Removing Artifacts from Neuronal Signals: An Empirical Analysis

Fabietti, Marcos; Mahmud, Mufti; Lotfi, Ahmad

doi:10.1007/978-3-030-59277-6_17

Cited by 9 publications

(2 citation statements)

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“…Multi-modality The incorporation of another source of information (e.g., sensor signal or video) can facilitate and improve the detection of artifacts [78]. A new module would allow the incorporation of such data to facilitate the labeling process or as part of a classification model's input.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence has been used for analysis of patterns and classification in diverse fields such as, anomaly detection [29,[36][37][38][39][40][41][42][43][44], biological data mining [45,46], disease detection [47][48][49][50][51][52][53][54][55][56][57][58], monitoring of human [59][60][61][62], financial forecasting [63], image analysis [64,65], and natural language processing [66][67][68]. Most of the time, these algorithms are composed of multiple layers of neurons for processing of non-linear information and were inspired by how the human brain works.…”

Section: Artifact Detectionmentioning

confidence: 99%

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SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

et al. 2021

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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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Section: Discussionmentioning

confidence: 99%