ow many people are able to control a P 300 - based brain – computer nterface ( BCI ) ?

An EEG-based brain–computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for 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. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N = 81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N = 38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8 h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89% of the 81 RC subjects were able to spell with accuracy 80–100%. A similar study using a motor imagery BCI with 99 subjects showed that only 19% of the subjects were able to achieve accuracy of 80–100%. These large differences in accuracy suggest that with limited amounts 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 ts wh be conducted with subjec brain–computer interface (BCI) allows people to use electroenephalographic (EEG) activity to control external devices such as obots, virtual environments, or spelling devices [6,16]. It is necssary to train BCI systems on subject-specific EEG data before ∗ Corresponding author at: Sierningstrasse 14, 4521 Schiedlberg, Austria. el.: +43 7251 22240 12; fax: +43 7251 22240 39. E-mail address: [email protected] (C. Guger). URL: http://www.gtec.at/ (C. Guger). 304-3940/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. oi:10.1016/j.neulet.2009.06.045 o have ALS to determine if their accuracy levels are similar. © 2009 Elsevier Ireland Ltd. All rights reserved. real-time BCI use is possible. Depending on the type of BCI being used, the amount of training time can vary from minutes to hours. Several different EEG signals can be used for BCI control. For example, slow cortical potentials [1], oscillations in alpha and beta range [6,11], steady-state visual evoked potentials (SSVEP) [2,17] and the P300 event-related potential [13] have all been used successfully for BCI control. BCI systems based on oscillations use mostly motor imagery strategies to generate event-related desynchronization (ERD) and event-related synchronization (ERS) in the alpha and beta frequency ranges of the EEG [9]. This type of BCI is