{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:34:58Z","timestamp":1742999698590,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031258909"},{"type":"electronic","value":"9783031258916"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25891-6_36","type":"book-chapter","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T14:03:34Z","timestamp":1678370614000},"page":"473-487","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Bayesian-Optimized Convolutional Neural Network to Decode Reach-to-Grasp from Macaque Dorsomedial Visual Stream"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3791-8555","authenticated-orcid":false,"given":"Davide","family":"Borra","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0730-4088","authenticated-orcid":false,"given":"Matteo","family":"Filippini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0911-0308","authenticated-orcid":false,"given":"Mauro","family":"Ursino","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0079-3755","authenticated-orcid":false,"given":"Patrizia","family":"Fattori","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4673-2974","authenticated-orcid":false,"given":"Elisa","family":"Magosso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195388855.001.0001","volume-title":"Brain-Computer Interfaces: Principles and Practice","author":"J Wolpaw","year":"2012","unstructured":"Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, USA (2012)"},{"key":"36_CR2","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neunet.2022.03.044","volume":"151","author":"M Filippini","year":"2022","unstructured":"Filippini, M., Borra, D., Ursino, M., Magosso, E., Fattori, P.: Decoding sensorimotor information from superior parietal lobule of macaque via Convolutional Neural Networks. Neural Netw. 151, 276\u2013294 (2022). https:\/\/doi.org\/10.1016\/j.neunet.2022.03.044","journal-title":"Neural Netw."},{"key":"36_CR3","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1523\/JNEUROSCI.3077-16.2017","volume":"37","author":"M Filippini","year":"2017","unstructured":"Filippini, M., Breveglieri, R., Akhras, M.A., Bosco, A., Chinellato, E., Fattori, P.: Decoding information for grasping from the macaque dorsomedial visual stream. J. Neurosci. 37, 4311\u20134322 (2017). https:\/\/doi.org\/10.1523\/JNEUROSCI.3077-16.2017","journal-title":"J. Neurosci."},{"key":"36_CR4","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1016\/j.celrep.2018.03.090","volume":"23","author":"M Filippini","year":"2018","unstructured":"Filippini, M., Breveglieri, R., Hadjidimitrakis, K., Bosco, A., Fattori, P.: Prediction of reach goals in depth and direction from the parietal cortex. Cell Rep. 23, 725\u2013732 (2018). https:\/\/doi.org\/10.1016\/j.celrep.2018.03.090","journal-title":"Cell Rep."},{"key":"36_CR5","doi-asserted-by":"publisher","first-page":"201","DOI":"10.3389\/fnhum.2019.00201","volume":"13","author":"AJ Solon","year":"2019","unstructured":"Solon, A.J., Lawhern, V.J., Touryan, J., McDaniel, J.R., Ries, A.J., Gordon, S.M.: Decoding P300 variability using convolutional neural networks. Front. Hum. Neurosci. 13, 201 (2019). https:\/\/doi.org\/10.3389\/fnhum.2019.00201","journal-title":"Front. Hum. Neurosci."},{"key":"36_CR6","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.neunet.2020.05.032","volume":"129","author":"D Borra","year":"2020","unstructured":"Borra, D., Fantozzi, S., Magosso, E.: Interpretable and lightweight convolutional neural network for EEG decoding: application to movement execution and imagination. Neural Netw. 129, 55\u201374 (2020). https:\/\/doi.org\/10.1016\/j.neunet.2020.05.032","journal-title":"Neural Netw."},{"key":"36_CR7","doi-asserted-by":"publisher","first-page":"655840","DOI":"10.3389\/fnhum.2021.655840","volume":"15","author":"D Borra","year":"2021","unstructured":"Borra, D., Fantozzi, S., Magosso, E.: A lightweight multi-scale convolutional neural network for p300 decoding: analysis of training strategies and uncovering of network decision. Front. Hum. Neurosci. 15, 655840 (2021). https:\/\/doi.org\/10.3389\/fnhum.2021.655840","journal-title":"Front. Hum. Neurosci."},{"key":"36_CR8","doi-asserted-by":"publisher","first-page":"791","DOI":"10.31083\/j.jin2004083","volume":"20","author":"D Borra","year":"2021","unstructured":"Borra, D., Magosso, E.: Deep learning-based EEG analysis: investigating P3 ERP components. J. Integr. Neurosci. 20, 791\u2013811 (2021). https:\/\/doi.org\/10.31083\/j.jin2004083","journal-title":"J. Integr. Neurosci."},{"key":"36_CR9","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1093\/bib\/bbaa355","volume":"22","author":"JA Livezey","year":"2021","unstructured":"Livezey, J.A., Glaser, J.I.: Deep learning approaches for neural decoding across architectures and recording modalities. Brief. Bioinform. 22, 1577\u20131591 (2021). https:\/\/doi.org\/10.1093\/bib\/bbaa355","journal-title":"Brief. Bioinform."},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16, 031001 (2019). https:\/\/doi.org\/10.1088\/1741-2552\/ab0ab5","journal-title":"J. Neural Eng."},{"key":"36_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8875426","volume":"2020","author":"NS Suhaimi","year":"2020","unstructured":"Suhaimi, N.S., Mountstephens, J., Teo, J.: EEG-based emotion recognition: a state-of-the-art review of current trends and opportunities. Comput. Intell. Neurosci. 2020, 1\u201319 (2020). https:\/\/doi.org\/10.1155\/2020\/8875426","journal-title":"Comput. Intell. Neurosci."},{"key":"36_CR12","doi-asserted-by":"publisher","first-page":"568104","DOI":"10.3389\/fnins.2020.568104","volume":"14","author":"M Sim\u00f5es","year":"2020","unstructured":"Sim\u00f5es, M., et al.: BCIAUT-P300: a multi-session and multi-subject benchmark dataset on autism for p300-based brain-computer-interfaces. Front. Neurosci. 14, 568104 (2020). https:\/\/doi.org\/10.3389\/fnins.2020.568104","journal-title":"Front. Neurosci."},{"key":"36_CR13","doi-asserted-by":"publisher","unstructured":"Borra, D., Magosso, E., Castelo-Branco, M., Simoes, M.: A Bayesian-optimized design for an interpretable convolutional neural network to decode and analyze the P300 response in autism. J. Neural Eng. 19 (2022). https:\/\/doi.org\/10.1088\/1741-2552\/ac7908","DOI":"10.1088\/1741-2552\/ac7908"},{"key":"36_CR14","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","volume":"38","author":"RT Schirrmeister","year":"2017","unstructured":"Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38, 5391\u20135420 (2017)","journal-title":"Hum. Brain Mapp."},{"key":"36_CR15","doi-asserted-by":"publisher","first-page":"12913","DOI":"10.1523\/JNEUROSCI.1463-08.2008","volume":"28","author":"GH Mulliken","year":"2008","unstructured":"Mulliken, G.H., Musallam, S., Andersen, R.A.: Decoding trajectories from posterior parietal cortex ensembles. J. Neurosci. 28, 12913\u201312926 (2008). https:\/\/doi.org\/10.1523\/JNEUROSCI.1463-08.2008","journal-title":"J. Neurosci."},{"key":"36_CR16","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1126\/science.aaa5417","volume":"348","author":"T Aflalo","year":"2015","unstructured":"Aflalo, T., et al.: Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348, 906\u2013910 (2015). https:\/\/doi.org\/10.1126\/science.aaa5417","journal-title":"Science"},{"key":"36_CR17","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1016\/j.neucom.2010.07.029","volume":"74","author":"E Chinellato","year":"2011","unstructured":"Chinellato, E., Grzyb, B.J., Marzocchi, N., Bosco, A., Fattori, P., del Pobil, A.P.: The Dorso-medial visual stream: from neural activation to sensorimotor interaction. Neurocomputing 74, 1203\u20131212 (2011). https:\/\/doi.org\/10.1016\/j.neucom.2010.07.029","journal-title":"Neurocomputing"},{"key":"36_CR18","doi-asserted-by":"publisher","unstructured":"Fattori, P., Breveglieri, R., Bosco, A., Gamberini, M., Galletti, C.: Vision for prehension in the medial parietal cortex. Cereb. Cortex. bhv302 (2015). https:\/\/doi.org\/10.1093\/cercor\/bhv302","DOI":"10.1093\/cercor\/bhv302"},{"key":"36_CR19","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F. and Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. pp. 448\u2013456. PMLR, Lille (2015)"},{"key":"36_CR20","unstructured":"Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint (2015)"},{"key":"36_CR21","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR22","unstructured":"Frazier, P.I.: A tutorial on Bayesian optimization (2018). http:\/\/arxiv.org\/abs\/1807.02811"},{"key":"36_CR23","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 [cs] (2017)"},{"key":"36_CR24","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neuroimage.2008.03.061","volume":"44","author":"S Smith","year":"2009","unstructured":"Smith, S., Nichols, T.: Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44, 83\u201398 (2009). https:\/\/doi.org\/10.1016\/j.neuroimage.2008.03.061","journal-title":"Neuroimage"},{"issue":"2","key":"36_CR25","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s40473-018-0151-z","volume":"5","author":"M Nowak","year":"2018","unstructured":"Nowak, M., Zich, C., Stagg, C.J.: Motor cortical gamma oscillations: what have we learnt and where are we headed? Curr. Behav. Neurosci. Rep. 5(2), 136\u2013142 (2018). https:\/\/doi.org\/10.1007\/s40473-018-0151-z","journal-title":"Curr. Behav. Neurosci. Rep."},{"key":"36_CR26","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv:1312.6034 [cs] (2014)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25891-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T10:24:09Z","timestamp":1680690249000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25891-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031258909","9783031258916"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25891-6_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Certosa di Pontignano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lod2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2022.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}