title: Comparing Features for Classification of MEG Responses to Motor Imagery.
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.
authors.affiliation: Department of Neuroscience and Biomedical Engineering (NBE), Aalto University School of Science, Espoo, Finland.|Radiology Unit, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki, Finland.
authors.forename: Hanna-Leena
authors.initials: HL
authors.lastname: Halme
authors.affiliation: Department of Neuroscience and Biomedical Engineering (NBE), Aalto University School of Science, Espoo, Finland.|Aalto Neuroimaging, MEG Core, Aalto University School of Science, Espoo, Finland.
authors.forename: Lauri
authors.initials: L
authors.lastname: Parkkonen
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D005260:Female; D007839:Functional Laterality; D006801:Humans; D019018:Imagery, Psychotherapy / Q000379:methods*; D007092:Imagination / Q000502:physiology*; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D009068:Movement / Q000502:physiology; D058765:Neurofeedback / Q000379:methods; D055815:Young Adult