Electromagnetic signals in noninvasive brain-computer interfaces

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Kauhanen, Laura
dc.date.accessioned 2012-08-20T06:28:55Z
dc.date.available 2012-08-20T06:28:55Z
dc.date.issued 2008
dc.identifier.isbn 978-951-22-9393-3
dc.identifier.isbn 978-951-22-9392-6 (printed) #8195;
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/4490
dc.description.abstract A brain-computer interface (BCI) translates task-related brain activity into computer commands. Detecting this activity is difficult, as the measured brain signals are generated by multiple sources and also include task-irrelevant brain activity. Using conventional methods such as signal averaging is not possible, because subjects should receive online feedback of their performance. BCI users usually either learn to control some components of their brain activity with the help of feedback or are presented with some stimuli that produce detectable signals in the brain. This thesis reviews BCI research, basic principles of electroencephalography (EEG) and magnetoencephalography (MEG), the sensorimotor cortex, and then describes experimental BCI studies. The thesis comprises of five publications studying 1) sensorimotor cortical activation for BCI, 2) use of MEG for BCIs, 3) single brain signal trials during (attempted) finger movements for online BCI classification and 4) vibrotactile feedback in comparison to visual feedback. Participants were 45 healthy, 9 tetraplegic, and 3 paraplegic subjects. First our results of tetraplegic subjects show that their 10- and 20-Hz rhythmic activity is more widespread and less contralateral than that of healthy subjects, providing a poorer control signal for two-class movement classification. For separating brain signals during right and left attempted movement, we selected features from the low-frequency bands. Second, our results show that for classification, MEG is not superior to EEG for two-class BCI, despite being a more localised measurement technique. Third, brain signals during finger movements could be classified online with high accuracy after basically no training. However, results from the tetraplegic subjects are much worse than those of the healthy subjects. Fourth, we show that vibrotactile feedback can be used as an alternative feedback channel during training and is especially useful when visual attention is needed for application control. On the basis of these and earlier findings, it is concluded that accurate control of noninvasive BCI is possible but requires some training. Future important research involves more work with motor-disabled patients, especially when testing new signal processing methods. Better performance may also be achieved using different feedback modalities. en
dc.format.extent Verkkokirja (1161 KB, 60 s.)
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Teknillinen korkeakoulu en
dc.relation.haspart [Publication 1]: L. Kauhanen, T. Nykopp, and M. Sams. 2006. Classification of single MEG trials related to left and right index finger movements. Clinical Neurophysiology, volume 117, number 2, pages 430-439. © 2006 Elsevier Science. By permission. en
dc.relation.haspart [Publication 2]: Laura Kauhanen, Tommi Nykopp, Janne Lehtonen, Pasi Jylänki, Jukka Heikkonen, Pekka Rantanen, Hannu Alaranta, and Mikko Sams. 2006. EEG and MEG brain–computer interface for tetraplegic patients. IEEE Transactions on Neural Systems and Rehabilitation Engineering, volume 14, number 2, pages 190-193. © 2006 IEEE. By permission. en
dc.relation.haspart [Publication 3]: J. Lehtonen, P. Jylänki, L. Kauhanen, and M. Sams. 2008. Online classification of single EEG trials during finger movements. IEEE Transactions on Biomedical Engineering, volume 55, number 2, pages 713-720. © 2008 IEEE. By permission. en
dc.relation.haspart [Publication 4]: Laura Kauhanen, Pasi Jylänki, Janne Lehtonen, Pekka Rantanen, Hannu Alaranta, and Mikko Sams. 2007. EEG-based brain-computer interface for tetraplegics. Computational Intelligence and Neuroscience, volume 2007, article ID 23864, 11 pages, doi:10.1155/2007/23864. © 2007 by authors. en
dc.relation.haspart [Publication 5]: Febo Cincotti, Laura Kauhanen, Fabio Aloise, Tapio Palomäki, Nicholas Caporusso, Pasi Jylänki, Donatella Mattia, Fabio Babiloni, Gerolf Vanacker, Marnix Nuttin, Maria Grazia Marciani, and José del R. Millán. 2007. Vibrotactile feedback for brain-computer interface operation. Computational Intelligence and Neuroscience, volume 2007, article ID 48937, 12 pages, doi:10.1155/2007/48937. © 2007 by authors. en
dc.subject.other Medical sciences en
dc.title Electromagnetic signals in noninvasive brain-computer interfaces en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.department Lääketieteellisen tekniikan ja laskennallisen tieteen laitos fi
dc.subject.keyword brain-computer interface en
dc.subject.keyword brain signal analysis en
dc.subject.keyword tetraplegic en
dc.subject.keyword EEG en
dc.subject.keyword MEG en
dc.subject.keyword feedback en
dc.identifier.urn URN:ISBN:978-951-22-9393-3
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en

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