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Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources.

, , и . BI, том 12241 из Lecture Notes in Computer Science, стр. 151-162. Springer, (2020)

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Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning., , и . Brain Informatics, 9 (1): 1-17 (2022)On-Chip Machine Learning for Portable Systems: Application to Electroencephalography-based Brain-Computer Interfaces., , и . IJCNN, стр. 1-8. IEEE, (2021)Signal Power Affects Artefact Detection Accuracy in Chronically Recorded Local Field Potentials: Preliminary Results., , , , , , и . NER, стр. 166-169. IEEE, (2021)Artefact Detection in Chronically Recorded Local Field Potentials: An Explainable Machine Learning-based Approach., , и . IJCNN, стр. 1-7. IEEE, (2022)Anomaly Detection in Invasively Recorded Neuronal Signals Using Deep Neural Network: Effect of Sampling Frequency., , и . AII, стр. 79-91. (2021)Adaptation of Convolutional Neural Networks for Multi-Channel Artifact Detection in Chronically Recorded Local Field Potentials., , , , , , и . SSCI, стр. 1607-1613. IEEE, (2020)Effectiveness of Employing Multimodal Signals in Removing Artifacts from Neuronal Signals: An Empirical Analysis., , и . BI, том 12241 из Lecture Notes in Computer Science, стр. 183-193. Springer, (2020)A Matlab-Based Open-Source Toolbox for Artefact Removal from Extracellular Neuronal Signals., , и . BI, том 12960 из Lecture Notes in Computer Science, стр. 351-365. Springer, (2021)Neural Network-based Artifact Detection in Local Field Potentials Recorded from Chronically Implanted Neural Probes., , , , , , и . IJCNN, стр. 1-8. IEEE, (2020)Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources., , и . BI, том 12241 из Lecture Notes in Computer Science, стр. 151-162. Springer, (2020)