How Many People are Able to Control a P300/Motor Imagery-Based Brain-Computer Interface?

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An EEG based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. P300 based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms. Motor imagery or SSVEP-based (SteadyState Visual Evoked Potential) systems are optimal to generate a continuous control signal. In this study, 81 subjects tested a P300 based and 99 subjects tested a motor imagery based BCI system. The subjects participating in the P300 study had to spell a 5 character word with only 5 minutes of training. EEG data were acquired to train the system while the subject looked at a 36 character matrix to spell the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. The subjects participating in the motor imagery study had to move 40 times a cursor to the right or left side of the computer monitor. Training and classifier calculation were performed with 40 imaginations of left and right hand movement initiated by an arrow pointing to the left and right side. For the P300 system 72.8 % were able to spell with 100 % accuracy and less than 3 % did not spell any character correctly [Guger 09]. For motor imagery 6.2 % achieved an accuracy above 90 % and 6.7 % performed with almost random classification accuracy between 50-59 % [Guger 2003]. It must be noted that for the P300 system the random classification accuracy is 1/36 % and for the motor imagery system it is 50 %. The training time for both systems was almost equal: 6 min for motor imagery, 5 min for P300 and also the montage time for the electrodes was almost equal (5 electrodes for motor imagery and 9 for the P300 system). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately five minutes of training data for a large number of non-disabled subjects. The large differences in accuracy between the two systems suggest that with limited amount of training data the P300 based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar. Summarizing it can be said that a P300 based system is suitable for spelling applications, but also e.g. for Smart Home control with several controllable devices. The motor imagery based system is suitable if a continuous control signal is needed.


Asilomar, CA

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