Items where Author is "Fabietti, M"

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Number of items: 10.

Journal article

FABIETTI, M., MAHMUD, M. and LOTFI, A., 2022. Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning. Brain Informatics, 9: 1 (2022). ISSN 2198-4018

FABIETTI, M., MAHMUD, M., LOTFI, A., KAISER, M.S., AVERNA, A., GUGGENMOS, D.J., NUDO, R.J., CHIAPPALONE, M. and CHEN, J., 2021. SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals. Brain Informatics, 8: 14. ISSN 2198-4018

Chapter in book

FABIETTI, M., MAHMUD, M. and LOTFI, A., 2021. On-chip machine learning for portable systems: application to electroencephalography-based brain-computer interfaces. In: 2021 International Joint Conference on Neural Networks (IJCNN) proceedings. IEEE. ISBN 9781665445979

FABIETTI, M., MAHMUD, M. and LOTFI, A., 2021. Anomaly detection in invasively recorded neuronal signals using deep neural network: effect of sampling frequency. In: M. MAHMUD, M. SHAMIM KAISER, N. KASABOV, K. IFTEKHARUDDIN and N. ZHONG, eds., Applied Intelligence and Informatics: first international conference, AII 2021, Nottingham, UK, July 30–31, 2021, Proceedings. Communications in Computer and Information Science (1435). Cham: Springer International Publishing, pp. 79-91. ISBN 9783030822682

FABIETTI, M., MAHMUD, M., LOTFI, A., AVERNA, A., GUGGENMOS, D., NUDO, R. and CHIAPPALONE, M., 2021. Signal power affects artefact detection accuracy in chronically recorded local field potentials: preliminary results. In: 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021. IEEE, pp. 166-169. ISBN 9781728143378

FABIETTI, M., MAHMUD, M. and LOTFI, A., 2021. A Matlab-based open-source toolbox for artefact removal from extracellular neuronal signals. In: M. MAHMUD, M. SHAMIM KAISER, S. VASSANELLI, Q. DAI and N. ZHONG, eds., Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. Lecture notes in computer science (12960). Cham: Springer International Publishing, pp. 351-365. ISBN 9783030869922

FABIETTI, M., MAHMUD, M., LOTFI, A., AVERNA, A., GUGGENMOS, D., NUDO, R. and CHIAPPALONE, M., 2020. Adaptation of convolutional neural networks for multi-channel artifact detection in chronically recorded local field potentials. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI) proceedings. Institute of Electrical and Electronics Engineers (IEEE), pp. 1607-1613. ISBN 9781728125473

FABIETTI, M., MAHMUD, M. and LOTFI, A., 2020. Effectiveness of employing multimodal signals in removing artifacts from neuronal signals: an empirical analysis. In: M. MAHMUD, S. VASSANELLI, M.S. KAISER and N. ZHONG, eds., Brain Informatics: 13th International Conference, BI 2020, Padua, Italy, September 19, 2020, Proceedings. Lecture Notes in Computer Science (12241). Springer International Publishing, pp. 183-193. ISBN 9783030592769

FABIETTI, M., MAHMUD, M. and LOTFI, A., 2020. Machine learning in analysing invasively recorded neuronal signals: available open access data sources. In: M. MAHMUD, S. VASSANELLI, M.S. KAISER and N. ZHONG, eds., Brain Informatics: 13th International Conference, BI 2020, Padua, Italy, September 19, 2020, Proceedings. Lecture Notes in Computer Science (12241). Springer International Publishing, pp. 151-162. ISBN 9783030592769

FABIETTI, M., MAHMUD, M., LOTFI, A., AVARUA, A., GUGGANMOS, D., XUDO, R. and CHIAPPALONE, M., 2020. Neural network-based artifact detection in local field potentials recorded from chronically implanted neural probes. In: 2020 International Joint Conference on Neural Networks (IJCNN). 2020 conference proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). ISBN 9781728169262

This list was generated on Mon Jun 27 23:49:50 2022 UTC.