Comparing features for classification of MEG responses to motor imagery

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Halme, Hanna Leena Parkkonen, Lauri 2017-03-23T12:49:46Z 2017-03-23T12:49:46Z 2016-12-01
dc.identifier.citation Halme , H L & Parkkonen , L 2016 , ' Comparing features for classification of MEG responses to motor imagery ' PLOS ONE , vol 11 , no. 12 , e0168766 , pp. 1-21 . DOI: 10.1371/journal.pone.0168766 en
dc.identifier.issn 1932-6203
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dc.description.abstract Background Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. Methods MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. Results The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. Conclusions We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system. en
dc.format.extent 1-21
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries PLOS ONE en
dc.relation.ispartofseries Volume 11, issue 12 en
dc.rights openAccess en
dc.subject.other Medicine(all) en
dc.subject.other Biochemistry, Genetics and Molecular Biology(all) en
dc.subject.other Agricultural and Biological Sciences(all) en
dc.subject.other 3112 Neurosciences en
dc.title Comparing features for classification of MEG responses to motor imagery en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Neuroscience and Biomedical Engineering
dc.subject.keyword Medicine(all)
dc.subject.keyword Biochemistry, Genetics and Molecular Biology(all)
dc.subject.keyword Agricultural and Biological Sciences(all)
dc.subject.keyword 3112 Neurosciences
dc.identifier.urn URN:NBN:fi:aalto-201703233200
dc.identifier.doi 10.1371/journal.pone.0168766
dc.type.version publishedVersion

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