Real-time machine learning of MEG: Decoding signatures of selective attention

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorParkkonen, Lauri
dc.contributor.authorJas, Mainak
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorParkkonen, Lauri
dc.date.accessioned2015-04-07T13:36:29Z
dc.date.available2015-04-07T13:36:29Z
dc.date.issued2015-03-30
dc.description.abstractBrain--computer interfaces (BCIs) provide disabled patients with access to communication tools and control of prosthetic devices. Most BCIs employ a machine-learning algorithm which analyzes brain data in real time and provides users with feedback. Magnetoencephalography (MEG) is a non-invasive method which records neuromagnetic signals from the brain at a high temporal resolution. This makes it particularly suitable for real-time analysis and machine learning. Developing tools that allow such analysis will have long-term benefits in using MEG for BCI approaches and exploring new experimental paradigms. In this thesis, a real-time analysis pipeline for machine learning in MEG was developed with the goal to enable BCI in MEG systems. The implementation details of the pipeline were described in the thesis along with performance details. Additionally, pilot measurements to decode auditory attention were conducted. The spatio-temporal dynamics of the offline experiment were used to optimize the preprocessing steps required for the BCI application. In particular, the frequency range of 1.0--1.5 Hz was found to be particularly discriminative. Finally, simulating this pipeline in pseudo real-time mode demonstrated that a BCI to decode auditory attention is feasible in MEG.en
dc.format.extent42
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/15550
dc.identifier.urnURN:NBN:fi:aalto-201504082214
dc.language.isoenen
dc.programmeMaster’s Programme in Machine Learning and Data Mining (Macadamia)fi
dc.programme.majorMachine Learning and Data Miningfi
dc.programme.mcodeSCI3015fi
dc.rights.accesslevelopenAccess
dc.subject.keywordmachine learningen
dc.subject.keywordselective attentionen
dc.subject.keywordbrain–computer interfaceen
dc.subject.keywordmagnetoencephalographyen
dc.subject.keywordreal-time analysisen
dc.titleReal-time machine learning of MEG: Decoding signatures of selective attentionen
dc.typeG2 Pro gradu, diplomityöen
dc.type.okmG2 Pro gradu, diplomityö
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
dc.type.publicationmasterThesis
local.aalto.idinssi51067
local.aalto.openaccessyes
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