%0 Journal Article
%T Online Detection of P300 and Error Potentials in a BCI Speller
%A Bernardo Dal Seno
%A Matteo Matteucci
%A Luca Mainardi
%J Computational Intelligence and Neuroscience
%D 2010
%I Hindawi Publishing Corporation
%R 10.1155/2010/307254
%X Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs. The developed system was tested on-line on three subjects and here we report preliminary results. In two out of three subjects, the GA provided a good performance in detecting P300 (90% and 60% accuracy with 5 repetitions), and it was possible to detect ErrP with an accuracy (roughly 60%) well above the chance level. In our knowledge, this is the first time that ErrP detection is performed on-line in a P300-based BCI. Preliminary results are encouraging, but further refinements are needed to improve performances. 1. Introduction A brain-computer interface (BCI) is an interface that does not entail muscle movements, but it bypasses any muscle or nerve mediation and connects a computer directly with the brain by picking up signals generated by the brain activity. Among the different kinds of brain activity that can be used in a BCI, the P300 phenomenon has been known [1] and investigated for many years. It is an event-related potential (ERP), traditionally described as a positive peak visible in an EEG recording at approximately 300£żms from an event. It follows unexpected, rare, or particularly informative stimuli, and it is typically stronger in the parietal area. The shape of the P300 depends on the characteristics of the stimuli and their presentation. For BCI applications, the Ħ°exactĦħ shape of the P300 is not so important as having a way to detect it. Detecting a P300 in a single trial is very difficult and, therefore, repeated stimuli are normally used to facilitate the selection of the one that has generated a P300. The number of repetitions can be predetermined for each user to get the best trade-off between speed and accuracy. In [2], Donchin and colleagues presented the first P300-based BCI, called also P300 speller, which permits to spell words. A grid of letters and symbols is presented to the user, and entire columns or rows are flashed one after the other in random order (see Figure 1 for an example). When the column/row containing the desired letter is flashed, a P300 is elicited. In Donchin's work, classification is made through stepwise discriminant analysis (SWDA) applied to averages of samples from epochs
%U http://www.hindawi.com/journals/cin/2010/307254/