Fusion of classic P300 detection methods' inferences in a framework of fuzzy labels

Designing a reliable and accurate brain-computer interface (BCI) is one of the most challenging fields in biomedical signal processing. To achieve this goal, different methods have been adopted in different blocks of a typical BCI system (i.e., in preprocessing, feature extraction, feature classification and feature selection blocks). Since BCI's speed plays a crucial role in its success in real-life applications, using mathematically simple techniques with accurate and reliable performance can improve this aspect of BCI systems' design.

In this paper, a new method is introduced, which combines information from different classic time series similarity measures, using a simple fuzzy fusion framework. This method is accurate and reliable in P300 (a positive event-related component occurring 300 ms after stimulus onset) detection. This framework is used to combine two computationally simple signal detection methods: "peak picking" and "template matching". Fusion takes place in the last step (decision-making step) by means of a fuzzy rule-base.

Compared to similar works on electroencephalogram-based (EEG-based) BCI datasets, in spite of being computationally simple, this new technique's performance is comparable to very complicated methods, like support vector machines. This research indicates that, using both spatial and temporal information content of EEG trials (from all electrodes or a subset of them), even under a non-complicated mathematical framework can yield an accurate and powerful classification.