Classification of motor imagery and execution signals with population-level feature sets: implications for probe design in fNIRS based BCI

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dc.contributor.authorErdogan, Sinem Burcu
dc.contributor.authorOzsarfati, Eran
dc.contributor.authorDilek, Burcu
dc.contributor.authorKadak, Kubra Sogukkanli
dc.contributor.authorHanoglu, Lutfu
dc.contributor.authorAkin, Ata
dc.date.accessioned2025-10-16T15:24:03Z
dc.date.issued2019
dc.identifier.doi10.1088/1741-2552/aafdca
dc.identifier.otherWOS:000459251100005
dc.identifier.urihttps://openaccess.acibadem.edu.tr/handle/11443/8055
dc.publisherIOP Publishing Ltd
dc.sourceJOURNAL OF NEURAL ENGINEERING
dc.subjectfunctional near infrared spectroscopy
dc.subjectbrain\\-computer interface
dc.subjectrandom forest
dc.subjectsupport vector machines
dc.subjectartificial neural networks
dc.subjectmotor imagery
dc.subjectmotor execution
dc.titleClassification of motor imagery and execution signals with population-level feature sets: implications for probe design in fNIRS based BCI
dc.typeArticle

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