Chapter 8 Neurofeedback and Brain-Computer Interface. Clinical Applications

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dc.contributor.authorBirbaumer, Niels
dc.contributor.authorRamos Murguialday, Ander
dc.contributor.authorWeber, Cornelia
dc.contributor.authorMontoya, Pedro
dc.contributor.editorRossini, Luca
dc.contributor.editorIzzo, Dario
dc.contributor.editorSummerer, Leopold
dc.contributor.institutionMedical Technologies
dc.date.accessioned2024-07-24T11:45:58Z
dc.date.available2024-07-24T11:45:58Z
dc.date.issued2009
dc.description.abstractMost of the research devoted to BMI development consists of methodological studies comparing different online mathematical algorithms, ranging from simple linear discriminant analysis (LDA) (Dornhege et al., 2007) to nonlinear artificial neural networks (ANNs) or support vector machine (SVM) classification. Single cell spiking for the reconstruction of hand movements requires different statistical solutions than electroencephalography (EEG)-rhythm classification for communication. In general, the algorithm for BMI applications is computationally simple and differences in classification accuracy between algorithms used for a particular purpose are small. Only a very limited number of clinical studies with neurological patients are available, most of them single case studies. The clinical target populations for BMI-treatment consist primarily of patients with amyotrophic lateral sclerosis (ALS) and severe CNS damage including spinal cord injuries and stroke resulting in substantial deficits in communication and motor function. However, an extensive body of literature started in the 1970s using neurofeedback training. Such training implemented to control various EEG-measures provided solid evidence of positive effects in patients with otherwise pharmacologically intractable epilepsy, attention deficit disorder, and hyperactivity ADHD. More recently, the successful introduction and testing of real-time fMRI and a NIRS-BMI opened an exciting field of interest in patients with psychopathological conditions.en
dc.description.sponsorshipThis work was supported by the Deutsche Forschungsgemeinschaft (DFG), Bundesministerium für Bildung und Forschung (BMBF, Bernstein-Center for Neurotechnology 01GQ0831), Fatronik, San Sebastian, Spain, Motorike, Cesarea, Israel. Pedro Montoya was supported by Spanish Ministry of Science and European Funds (FEDER) (grant SEJ2007–62312).
dc.description.statusPeer reviewed
dc.format.extent11
dc.identifier.citationBirbaumer , N , Ramos Murguialday , A , Weber , C & Montoya , P 2009 , Chapter 8 Neurofeedback and Brain-Computer Interface. Clinical Applications . in L Rossini , D Izzo & L Summerer (eds) , International Review of Neurobiology . International Review of Neurobiology , vol. 86 , pp. 107-117 . https://doi.org/10.1016/S0074-7742(09)86008-X
dc.identifier.doi10.1016/S0074-7742(09)86008-X
dc.identifier.isbn9780123748218
dc.identifier.issn0074-7742
dc.identifier.urihttps://hdl.handle.net/11556/1514
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=67650248609&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofInternational Review of Neurobiology
dc.relation.ispartofseriesInternational Review of Neurobiology
dc.relation.projectIDSpanish Ministry of Science and European Funds
dc.relation.projectIDDeutsche Forschungsgemeinschaft, DFG, 67560623
dc.relation.projectIDBundesministerium für Bildung und Forschung, BMBF, 01GQ0831
dc.relation.projectIDEuropean Regional Development Fund, FEDER, SEJ2007–62312
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsNeurology (clinical)
dc.subject.keywordsCellular and Molecular Neuroscience
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.titleChapter 8 Neurofeedback and Brain-Computer Interface. Clinical Applicationsen
dc.typebook part
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