single trial classification of motor imagination using 6 dry eeg electrodes单电机试验分类想象使用6干脑电图电极.pdf

Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes 1 1 1 1,2 ¨ 1,2 Florin Popescu *, Siamac Fazli , Yakob Badower , Benjamin Blankertz , Klaus-R. Muller 1 Intelligent Data Analysis Laboratory, Fraunhofer Institute FIRST, Berlin, Germany, 2 Machine Learning Laboratory, Technical University Berlin, Berlin, Germany 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. Methodology/Principal Findings. 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/Significance. 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 mi