{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:34:11Z","timestamp":1766050451982,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434266"},{"type":"electronic","value":"9783031434273"}],"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\/978-3-031-43427-3_23","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:01:41Z","timestamp":1694898101000},"page":"379-393","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning for\u00a0Real-Time Neural Decoding of\u00a0Grasp"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8947-9481","authenticated-orcid":false,"given":"Paolo","family":"Viviani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilaria","family":"Gesmundo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elios","family":"Ghinato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8528-3753","authenticated-orcid":false,"given":"Andres","family":"Agudelo-Toro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0562-3157","authenticated-orcid":false,"given":"Chiara","family":"Vercellino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3056-796X","authenticated-orcid":false,"given":"Giacomo","family":"Vitali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6462-1699","authenticated-orcid":false,"given":"Letizia","family":"Bergamasco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8138-9403","authenticated-orcid":false,"given":"Alberto","family":"Scionti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7626-1563","authenticated-orcid":false,"given":"Marco","family":"Ghislieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5887-1499","authenticated-orcid":false,"given":"Valentina","family":"Agostini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8482-2607","authenticated-orcid":false,"given":"Olivier","family":"Terzo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6593-2800","authenticated-orcid":false,"given":"Hansj\u00f6rg","family":"Scherberger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","unstructured":"Ahmadi, N., Constandinou, T.G., Bouganis, C.S.: Decoding hand kinematics from local field potentials using long short-term memory (LSTM) network. In: 2019 9th International IEEE\/EMBS Conference on Neural Engineering (NER), pp. 415\u2013419. IEEE, San Francisco (2019). https:\/\/doi.org\/10.1109\/NER.2019.8717045","DOI":"10.1109\/NER.2019.8717045"},{"issue":"2","key":"23_CR2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aa9ee7","volume":"15","author":"DM Brandman","year":"2018","unstructured":"Brandman, D.M., et al.: Rapid calibration of an intracortical brain-computer interface for people with tetraplegia. J. Neural Eng. 15(2), 026007 (2018). https:\/\/doi.org\/10.1088\/1741-2552\/aa9ee7","journal-title":"J. Neural Eng."},{"key":"23_CR3","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1162\/tacl\\_a_00104","volume":"4","author":"JP Chiu","year":"2016","unstructured":"Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357\u2013370 (2016). https:\/\/doi.org\/10.1162\/tacl_a_00104","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"23_CR4","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"23_CR5","unstructured":"Fabiani, F.: Brain-Computer Interface for Bionic Prosthetic Arm Actuation. Master\u2019s thesis, Politecnico di Torino, Torino, Italy (2021)"},{"key":"23_CR6","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3389\/fninf.2014.00010","volume":"8","author":"S Garcia","year":"2014","unstructured":"Garcia, S., et al.: Neo: an object model for handling electrophysiology data in multiple formats. Front. Neuroinf. 8, 10 (2014). https:\/\/doi.org\/10.3389\/fninf.2014.00010","journal-title":"Front. Neuroinf."},{"key":"23_CR7","doi-asserted-by":"publisher","unstructured":"Glaser, J.I., Benjamin, A.S., Chowdhury, R.H., Perich, M.G., Miller, L.E., Kording, K.P.: Machine Learning for Neural Decoding. Eneuro 7(4) (2020). https:\/\/doi.org\/10.1523\/ENEURO.0506-19.2020","DOI":"10.1523\/ENEURO.0506-19.2020"},{"issue":"2","key":"23_CR8","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. Bioinf. 22(2), 1577\u20131591 (2021). https:\/\/doi.org\/10.1093\/bib\/bbaa355","journal-title":"Brief. Bioinf."},{"issue":"5","key":"23_CR9","doi-asserted-by":"publisher","first-page":"056016","DOI":"10.1088\/1741-2560\/12\/5\/056016","volume":"12","author":"VK Menz","year":"2015","unstructured":"Menz, V.K., Schaffelhofer, S., Scherberger, H.: Representation of continuous hand and arm movements in macaque areas M1, F5, and AIP: a comparative decoding study. J. Neural Eng. 12(5), 056016 (2015). https:\/\/doi.org\/10.1088\/1741-2560\/12\/5\/056016","journal-title":"J. Neural Eng."},{"key":"23_CR10","unstructured":"O\u2019Malley, T., et al.: Keras Tuner (2019). https:\/\/github.com\/keras-team\/keras-tuner"},{"key":"23_CR11","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1152\/physrev.00034.2020","volume":"102","author":"C Pandarinath","year":"2021","unstructured":"Pandarinath, C., Bensmaia, S.J.: The science and engineering behind sensitized brain-controlled bionic hands. Physiol. Rev. 102, 551\u2013604 (2021). https:\/\/doi.org\/10.1152\/physrev.00034.2020","journal-title":"Physiol. Rev."},{"issue":"3","key":"23_CR12","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1523\/JNEUROSCI.3594-14.2015","volume":"35","author":"S Schaffelhofer","year":"2015","unstructured":"Schaffelhofer, S., Agudelo-Toro, A., Scherberger, H.: Decoding a wide range of hand configurations from macaque motor, premotor, and parietal cortices. J. Neurosci. 35(3), 1068\u20131081 (2015). https:\/\/doi.org\/10.1523\/JNEUROSCI.3594-14.2015","journal-title":"J. Neurosci."},{"issue":"11","key":"23_CR13","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997). https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans. Signal Process."},{"key":"23_CR14","unstructured":"Yoo, S.H., Woo, S.W., Amad, Z.: Classification of three categories from prefrontal cortex using LSTM networks: fNIRS study. In: 2018 18th International Conference on Control, Automation and Systems (ICCAS), pp. 1141\u20131146 (2018)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43427-3_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:04:49Z","timestamp":1694898289000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43427-3_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434266","9783031434273"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43427-3_23","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":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"A comprehensive discussion of the ethical aspect of BMIs is out of the scope of this paper: the B-Cratos project has a dedicated ethics advisory board and provides an ethics report to the European Commission yearly. With respect to animal experimentation, the data discussed here has been acquired long before the start of this work and they were subject to regulations and ethics assessment.On the other hand, ethics topics are particularly relevant when dealing with ML algorithms for BMI use: in this work, for instance, different hyperparameters configurations were needed by animal Z (female) and animal M (male). For a future clinical use, assuming that a single model architecture would fit all users may introduce unwanted biases and negatively affect the user experience of patients.Finally, several privacy and security aspects have been discussed in relation to this application: from anonymised data storage that can prevent the association between patients brain recordings and their identity, up to the possibility of adversarial actors affecting the prosthesis movement by exploiting weaknesses specifically related to the ML model (i.e., by injecting malicious training data) and the potential mitigation actions. These discussions are also reported in the ethics deliverable submitted to the EC and not yet publicly accessible.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","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":"196","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":"24% - 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":"3.63","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":"4.5","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)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}