title: EEG and MEG brain-computer interface for tetraplegic patients.
abstract: We characterized features of magnetoencephalographic (MEG) and electroencephalographic (EEG) signals generated in the sensorimotor cortex of three tetraplegics attempting index finger movements. Single MEG and EEG trials were classified offline into two classes using two different classifiers, a batch trained classifier and a dynamic classifier. Classification accuracies obtained with dynamic classifier were better, at 75%, 89%, and 91% in different subjects, when features were in the 0.5-3.0-Hz frequency band. Classification accuracies of EEG and MEG did not differ.
authors.affiliation: Laboratory of Computational Engineering, Helsinki University of Technology, FIN-02015 Espoo, Finland. laura.kauhanen@tkk.fi
authors.forename: Laura
authors.initials: L
authors.lastname: Kauhanen
authors.forename: Tommi
authors.initials: T
authors.lastname: Nykopp
authors.forename: Janne
authors.initials: J
authors.lastname: Lehtonen
authors.forename: Pasi
authors.initials: P
authors.lastname: Jylänki
authors.forename: Jukka
authors.initials: J
authors.lastname: Heikkonen
authors.forename: Pekka
authors.initials: P
authors.lastname: Rantanen
authors.forename: Hannu
authors.initials: H
authors.lastname: Alaranta
authors.forename: Mikko
authors.initials: M
authors.lastname: Sams
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000503:physiopathology*; D016000:Cluster Analysis; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D011782:Quadriplegia / Q000503:physiopathology* / Q000534:rehabilitation*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D013813:Therapy, Computer-Assisted / Q000379:methods*