المؤلفون: Halme, Hanna-Leena

المساهمون: Parkkonen, Lauri, Prof., Aalto University School of Science, Department of Neuroscience and Biomedical Engineering, Finland, Perustieteiden korkeakoulu, School of Science, Neurotieteen ja lääketieteellisen tekniikan laitos, Department of Neuroscience and Biomedical Engineering, Aalto-yliopisto, Aalto University

مصطلحات موضوعية: Computer science, Medical sciences, MEG, motor imagery, brain–computer interface, sensorimotor rhythm, aivokäyttöliittymä, liikkeen kuvittelu, sensorimotorinen rytmi

وصف الملف: application/pdf

العلاقة: Aalto University publication series DOCTORAL THESES; 164/2022; [Publication 1]: Hanna-Leena Halme, Lauri Parkkonen. Comparing Features for Classification of MEG Responses to Motor Imagery. PLOS ONE, 11(12):e0168766, 12 2016. DOI:10.1371/journal.pone.0168766; [Publication 2]: Hanna-Leena Halme, Lauri Parkkonen. Across-subject offline decoding of motor imagery from MEG and EEG. Scientific Reports, 07 2018. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201808014095Test. DOI:10.1038/s41598-018-28295-z; [Publication 3]: Hanna-Leena Halme, Lauri Parkkonen. The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training. PLOS ONE, 17(2): e0264354, 02 2022. DOI:10.1371/journal.pone.0264354; 1799-4942 (electronic); 1799-4934 (printed); 1799-4934 (ISSN-L); https://aaltodoc.aalto.fi/handle/123456789/117740Test; URN:ISBN:978-952-64-1015-9

الإتاحة: https://doi.org/10.1371/journal.pone.0168766Test
https://doi.org/10.1038/s41598-018-28295-zTest
https://doi.org/10.1371/journal.pone.0264354Test
https://aaltodoc.aalto.fi/handle/123456789/117740Test

المؤلفون: Laiho, Jaakko

المساهمون: Siervo, Rosa, Perustieteiden korkeakoulu, Jung, Alexander, Aalto-yliopisto, Aalto University

مصطلحات موضوعية: brain-computer interfaces, convolutional neural networks, motor imagery, deep learning, transfer learning, electroencephalography

وصف الملف: application/pdf

العلاقة: https://aaltodoc.aalto.fi/handle/123456789/46105Test; URN:NBN:fi:aalto-202008235037

الإتاحة: https://aaltodoc.aalto.fi/handle/123456789/46105Test

المؤلفون: Halme, Hanna-Leena

المساهمون: Parkkonen, Lauri, Perustieteiden korkeakoulu, School of Science, Neurotieteen ja lääketieteellisen tekniikan laitos, Department of Neuroscience and Biomedical Engineering, Aalto-yliopisto, Aalto University

مصطلحات موضوعية: Electrical engineering, Medical sciences, magnetoencephalography, brain–computer interface, MEG, BCI, motor imagery, sensorimotor rhythm, magnetoenkefalografia, aivo-tietokone–käyttöliittymä, liikkeen kuvittelu, sensorimotorinen rytmi

وصف الملف: application/pdf

العلاقة: [Publication 1]: Hanna-Leena Halme, Lauri Parkkonen. Comparing Features for classification of MEG Responses to Motor Imagery. PLOS ONE, 11(12): e0168766, 2016. DOI:10.1371/journal.pone.0168766; [Publication 2]: Hanna-Leena Halme, Lauri Parkkonen. Across-subject offline decoding of motor imagery from MEG and EEG. Scientific Reports, 8(1): 10087, 2018. DOI:10.1038/s41598-018-28295-z; https://aaltodoc.aalto.fi/handle/123456789/37835Test; URN:NBN:fi:aalto-201905082945

الإتاحة: https://doi.org/10.1371/journal.pone.0168766Test
https://doi.org/10.1038/s41598-018-28295-zTest
https://aaltodoc.aalto.fi/handle/123456789/37835Test

المؤلفون: Auranen, Toni

المساهمون: Krause, Christina, Teknillinen korkeakoulu, Helsinki University of Technology, Sähkö- ja tietoliikennetekniikan osasto, Sams, Mikko, Aalto-yliopisto, Aalto University

مصطلحات موضوعية: nonparametric statistics, Quade test, magnetoencephalography, time-frequency representations, finger movement, motor imagery, epäparametrinen tilastotiede, Quaden testi, magnetoenkefalografia, aika-taajuusesitykset, sormien liikutus, motorinen kuvittelu

العلاقة: https://aaltodoc.aalto.fi/handle/123456789/90149Test; URN:NBN:fi:aalto-2020120448984

الإتاحة: https://aaltodoc.aalto.fi/handle/123456789/90149Test