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

Fabietti, Marcos; Mahmud, Mufti; Lotfi, Ahmad; Kaiser, M. Shamim; Averna, Alberto; Guggenmos, David J.; Nudo, Randolph J.; Chiappalone, Michela; Chen, Jianhui

doi:10.1186/s40708-021-00135-3

Cited by 13 publications

(4 citation statements)

References 80 publications

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“…Afterwards, the generated signals were compared in the temporal and spectral domains, where they mimic the properties of the physiological recordings. These approaches have been compiled into an open-access toolbox for artefact detection and removal [78].…”

Section: Local Field Potentialsmentioning

confidence: 99%

ABOT: An Open-Source Online Benchmarking Tool for Machine Learning-based Artefact Detection and Removal Methods from Neuronal Signals

Fabietti

Mahmud

Lotfi

et al. 2022

Preprint

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Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML's popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

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Section: Local Field Potentialsmentioning

confidence: 99%

ABOT: An Open-Source Online Benchmarking Tool for Machine Learning-based Artefact Detection and Removal Methods from Neuronal Signals

Fabietti

Mahmud

Lotfi

et al. 2022

Preprint

Self Cite

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“…In MATLAB, a built-in function called patternet is available. This function creates an MLP neural network with backpropagation according to user-specified layers and perceptrons [50,51]. Figure 5 shows a flow diagram of a simple two-layer MLP with perceptrons 4 and 3.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

Okojie¹,

Idoko²,

Herbert³

et al. 2021

ASI

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Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.

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“…During the online phase, the DLN model acted as a filter to automatically remove OAs from the corrupted EEG data. In [34], a MATLAB-based open-source toolbox that uses machine learning strategies based on neural networks to label and train models for detecting artefacts in invasive neuronal signals. The authors of [35] use CNN to select channels, after which multiple classes of motor imagery intentions are decoded.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Spike Sorting With Improved Channel Selection and Artefact Removal

2023

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In order to implement highly efficient brain-machine interface (BMI) systems, high-channel count sensing is often used to record extracellular action potentials. However, the extracellular recordings are typically severely contaminated by artefacts and various noise sources, rendering the separation of multiunit neural recordings an immensely challenging task. Removing artefact and noise from neural events can improve the spike sorting performance and classification accuracy. This paper presents a deep learning technique called deep spike detection (DSD) with a strong learning ability of high-dimensional vectors for neural channel selection and artefacts removal from the selected neural channel. The proposed method significantly improves spike detection compared to the conventional methods by sequentially diminishing the noise level and discarding the active artefacts in the recording channels. The simulated and experimental results show that there is considerably better performance when the extracellular raw recordings are cleaned prior to assigning individual spikes to the neurons that generated them. The DSD achieves an overall classification accuracy of 91.53% and outperformes Wave_clus by 3.38% on the simulated dataset with various noise levels and artefacts.INDEX TERMS Artefact removal, channel selection, convolutional neural network (CNN), deep earning, extracellular recordings, real-time sorting, spike sorting, .

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

Cited by 13 publications

References 80 publications

ABOT: An Open-Source Online Benchmarking Tool for Machine Learning-based Artefact Detection and Removal Methods from Neuronal Signals

ABOT: An Open-Source Online Benchmarking Tool for Machine Learning-based Artefact Detection and Removal Methods from Neuronal Signals

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

A Deep Neural Network-Based Spike Sorting With Improved Channel Selection and Artefact Removal

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