EEG and MEG brain-computer interface for tetraplegic patients.
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.
Kauhanen
Laura
L
Laboratory of Computational Engineering, Helsinki University of Technology, FIN-02015 Espoo, Finland. laura.kauhanen@tkk.fi
Nykopp
Tommi
T
Lehtonen
Janne
J
Jylänki
Pasi
P
Heikkonen
Jukka
J
Rantanen
Pekka
P
Alaranta
Hannu
H
Sams
Mikko
M
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*