| Publikationstyp: | Buchbeitrag |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | General principles of machine learning for brain-computer interfacing |
| Autor/-in: | Iturrate, Iñaki Chavarriaga, Ricardo Millán, José del R. |
| et. al: | No |
| DOI: | 10.1016/B978-0-444-63934-9.00023-8 |
| Erschienen in: | Handbook of Clinical Neurology ; 168 |
| Herausgeber/-in des übergeordneten Werkes: | Millan, José del R Ramsay, Nick F. |
| Seite(n): | 311 |
| Seiten bis: | 328 |
| Erscheinungsdatum: | Mär-2020 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISBN: | 978-0-444-63934-9 |
| Sprache: | Englisch |
| Schlagwörter: | Artifact; Brain-computer interface; Brain–machine interface; Classification; Cross-validation; Feature; Filtering; Information transfer rate; Machine learning; Performance evaluation; Regression |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/20278 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
Es gibt keine Dateien zu dieser Ressource.
Zur Langanzeige
Iturrate, I., Chavarriaga, R., & Millán, J. d. R. (2020). General principles of machine learning for brain-computer interfacing. In J. d. R. Millan & N. F. Ramsay (Eds.), Handbook of Clinical Neurology ; 168 (pp. 311–328). Elsevier. https://doi.org/10.1016/B978-0-444-63934-9.00023-8
Iturrate, I., Chavarriaga, R. and Millán, J.d.R. (2020) ‘General principles of machine learning for brain-computer interfacing’, in J.d.R. Millan and N.F. Ramsay (eds) Handbook of Clinical Neurology ; 168. Elsevier, pp. 311–328. Available at: https://doi.org/10.1016/B978-0-444-63934-9.00023-8.
I. Iturrate, R. Chavarriaga, and J. d. R. Millán, “General principles of machine learning for brain-computer interfacing,” in Handbook of Clinical Neurology ; 168, J. d. R. Millan and N. F. Ramsay, Eds. Elsevier, 2020, pp. 311–328. doi: 10.1016/B978-0-444-63934-9.00023-8.
ITURRATE, Iñaki, Ricardo CHAVARRIAGA und José del R. MILLÁN, 2020. General principles of machine learning for brain-computer interfacing. In: José del R MILLAN und Nick F. RAMSAY (Hrsg.), Handbook of Clinical Neurology ; 168. Elsevier. S. 311–328. ISBN 978-0-444-63934-9
Iturrate, Iñaki, Ricardo Chavarriaga, and José del R. Millán. 2020. “General Principles of Machine Learning for Brain-Computer Interfacing.” In Handbook of Clinical Neurology ; 168, edited by José del R Millan and Nick F. Ramsay, 311–28. Elsevier. https://doi.org/10.1016/B978-0-444-63934-9.00023-8.
Iturrate, Iñaki, et al. “General Principles of Machine Learning for Brain-Computer Interfacing.” Handbook of Clinical Neurology ; 168, edited by José del R Millan and Nick F. Ramsay, Elsevier, 2020, pp. 311–28, https://doi.org/10.1016/B978-0-444-63934-9.00023-8.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.