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Online detection of P300 and error potentials in a BCI speller.

Dal Seno B, Matteucci M, Mainardi L - Comput Intell Neurosci (2010)

Bottom Line: The developed system was tested on-line on three subjects and here we report preliminary results.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.

View Article: PubMed Central - PubMed

Affiliation: IIT-Unit, Department of Electronics and Information, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. bernardo.dalseno@polimi.it

ABSTRACT
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.

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Weight functions used for feature extraction.
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Related In: Results  -  Collection


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fig2: Weight functions used for feature extraction.

Mentions: In the genetic algorithm used in this work, each individual (chromosome) represents a set of possible features for discriminating the presence of a P300 in EEG recordings. Each gene encodes a feature and an EEG channel from which to extract it; a feature is obtained by multiplying the EEG channel by a weight function, whose exact shape is encoded by parameters in genes (see Figure 2 for examples of weight functions). Genetic operators are a variant of 1-point crossover and uniform mutation, and tournament selection with elitism is used. The fitness of a chromosome is the 4-fold cross-validated performance obtained by training a logistic classifier on the encoded features extracted from the training set. For a complete description of the algorithm, please see [15].


Online detection of P300 and error potentials in a BCI speller.

Dal Seno B, Matteucci M, Mainardi L - Comput Intell Neurosci (2010)

Weight functions used for feature extraction.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2821756&req=5

fig2: Weight functions used for feature extraction.
Mentions: In the genetic algorithm used in this work, each individual (chromosome) represents a set of possible features for discriminating the presence of a P300 in EEG recordings. Each gene encodes a feature and an EEG channel from which to extract it; a feature is obtained by multiplying the EEG channel by a weight function, whose exact shape is encoded by parameters in genes (see Figure 2 for examples of weight functions). Genetic operators are a variant of 1-point crossover and uniform mutation, and tournament selection with elitism is used. The fitness of a chromosome is the 4-fold cross-validated performance obtained by training a logistic classifier on the encoded features extracted from the training set. For a complete description of the algorithm, please see [15].

Bottom Line: The developed system was tested on-line on three subjects and here we report preliminary results.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.

View Article: PubMed Central - PubMed

Affiliation: IIT-Unit, Department of Electronics and Information, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. bernardo.dalseno@polimi.it

ABSTRACT
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.

Show MeSH