Single trial classification of motor imagination using 6 dry EEG electrodes.
BACKGROUND
Brain computer interfaces (BCI) based on electro-encephalography (EEG) have been shown to detect mental states accurately and non-invasively, but the equipment required so far is cumbersome and the resulting signal is difficult to analyze. BCI requires accurate classification of small amplitude brain signal components in single trials from recordings which can be compromised by currents induced by muscle activity.


RESULTS
A novel EEG cap based on dry electrodes was developed which does not need time-consuming gel application and uses far fewer electrodes than on a standard EEG cap set-up. After optimizing the placement of the 6 dry electrodes through off-line analysis of standard cap experiments, dry cap performance was tested in the context of a well established BCI cursor control paradigm in 5 healthy subjects using analysis methods which do not necessitate user training. The resulting information transfer rate was on average about 30% slower than the standard cap. The potential contribution of involuntary muscle activity artifact to the BCI control signal was found to be inconsequential, while the detected signal was consistent with brain activity originating near the motor cortex.


CONCLUSIONS
Our study shows that a surprisingly simple and convenient method of brain activity imaging is possible, and that simple and robust analysis techniques exist which discriminate among mental states in single trials. Within 15 minutes the dry BCI device is set-up, calibrated and ready to use. Peak performance matched reported EEG BCI state of the art in one subject. The results promise a practical non-invasive BCI solution for severely paralyzed patients, without the bottleneck of setup effort and limited recording duration that hampers current EEG recording technique. The presented recording method itself, BCI not considered, could significantly widen the use of EEG for emerging applications requiring long-term brain activity and mental state monitoring.
Popescu
Florin
F
Intelligent Data Analysis Laboratory, Fraunhofer Institute FIRST, Berlin, Germany. florin.popescu@first.fhg.de
Fazli
Siamac
S
Badower
Yakob
Y
Blankertz
Benjamin
B
Müller
Klaus-R
KR
D001921:Brain / Q000502:physiology*; D003463:Cues; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D016743:Mental Competency; D008606:Mental Processes / Q000502:physiology*; D009043:Motor Activity / Q000502:physiology*; D018579:Patient Selection; D015398:Signal Transduction; D014584:User-Computer Interface