Marcos Fabietti
Orcid: 0000-0003-3093-5985Affiliations:
- Nottingham Trent University, UK
According to our database1,
Marcos Fabietti
authored at least 15 papers
between 2020 and 2022.
Collaborative distances:
Collaborative distances:
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Bibliography
2022
ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals.
Brain Informatics, 2022
Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning.
Brain Informatics, 2022
Artefact Detection in Chronically Recorded Local Field Potentials: An Explainable Machine Learning-based Approach.
Proceedings of the International Joint Conference on Neural Networks, 2022
Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning.
Proceedings of the Brain Informatics - 15th International Conference, 2022
2021
SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals.
Brain Informatics, 2021
Interpretable Model for Artefact Detection in Local Field Potentials via Feature Extraction and Decision Trees.
Proceedings of the Advances in Computational Intelligence Systems, 2021
Signal Power Affects Artefact Detection Accuracy in Chronically Recorded Local Field Potentials: Preliminary Results.
Proceedings of the 10th International IEEE/EMBS Conference on Neural Engineering, 2021
On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces.
Proceedings of the International Joint Conference on Neural Networks, 2021
A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals.
Proceedings of the Brain Informatics - 14th International Conference, 2021
Anomaly Detection in Invasively Recorded Neuronal Signals Using Deep Neural Network: Effect of Sampling Frequency.
AII, 2021
2020
Adaptation of Convolutional Neural Networks for Multi-Channel Artifact Detection in Chronically Recorded Local Field Potentials.
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
Effectiveness of Employing Multimodal Signals in Removing Artifacts from Neuronal Signals: An Empirical Analysis.
Proceedings of the Brain Informatics - 13th International Conference, 2020
Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources.
Proceedings of the Brain Informatics - 13th International Conference, 2020