Neural networks for single trial P300 detection

P300 brain-computer interfaces have been gaining attention in recent years. To achieve good performance and accuracy, it is necessary to optimize both feature extraction and classification algorithms. This article aims at verifying whether supervised learning models based on self-organizing maps or adaptive resonance theory can be useful for this task. For feature extraction, the state-of-the-art Windowed means paradigm was used. For classification, proposed classifiers were compared with two traditional classifiers: linear discriminant analysis and multi-layer perceptron. Fifteen healthy subjects participated in the experiment.