Title :
EEG and MEG brain-computer interface for tetraplegic patients
Author :
Kauhanen, Laura ; Nykopp, Tommi ; Lehtonen, Janne ; Jylänki, Pasi ; Heikkonen, Jukka ; Rantanen, Pekka ; Alaranta, Hannu ; Sams, Mikko
Author_Institution :
Lab. of Computational Eng., Helsinki Univ. of Technol., Finland
fDate :
6/1/2006 12:00:00 AM
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.
Keywords :
biomechanics; electroencephalography; handicapped aids; magnetoencephalography; medical signal processing; signal classification; EEG; MEG; batch trained classifier; brain-computer interface; dynamic classifier; electroencephalographic signals; index finger movements; magnetoencephalographic signals; sensorimotor cortex; tetraplegic patients; Brain computer interfaces; Character generation; Educational robots; Electroencephalography; Fingers; Frequency; Laboratories; Rehabilitation robotics; Rhythm; Signal generators; Brain–computer interface (BCI); MEG; dynamic classification; electroencephalographic (EEG); tetraplegia; Artificial Intelligence; Brain; Cluster Analysis; Communication Aids for Disabled; Electroencephalography; Evoked Potentials; Humans; Magnetoencephalography; Male; Pattern Recognition, Automated; Quadriplegia; Reproducibility of Results; Sensitivity and Specificity; Software; Therapy, Computer-Assisted;
Journal_Title :
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
DOI :
10.1109/TNSRE.2006.875546
