Self-labeling for P300 detection

The P300 wave refers to a positive peak with a latency of 300 ms, produced in response to task-relevant stimuli. It is a widely-used event related potential (ERP) in practical brain computer interface (BCI) systems, where a classifier is trained using discriminative features extracted from a set of labeled examples, in order to detect the presence of P300. Given a small training examples, the intra- and inter-subject variations in amplitude and latency of P300 degrade the performance of classifier. Thus, it requires a longer training period (calibration time) with more labeled examples for satisfactory performance. In this paper we present a self-labeling method, where confident unlabeled data, together with their predicted labels, are gradually added to the training set, in order to re-train the classifier. Linear discriminant analysis with singular value decomposition is used to progressively extract discriminate features. Experiments demonstrate the high performance of our method, especially in the case where a small number of training examples are available. We also apply the method to the zero-calibration P300-based BCI, which removes subject-dependent calibration procedures by using the training set already recorded from other subjects.