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In the past decade, many researchers have dedicated their efforts to exploring brain computer interface (BCI) technology. With a growing number of investigations into the BCI and its related areas, BCI systems nowadays are not only developed for the disabled but also for the normal and healthy people. In this paper, a signal-processing-based technique with its applications into the development of automated character recognition is introduced. The task of the pattern recognition to such a BCI design problem was mainly accomplished based on the detection of P300 evoked potentials. We approached this detection problem by employing a template-matching-based method to extract the morphological information from EEG signals first, and then applying a linear discriminant function (LDF) to the features selected for pattern classification. The entire detection process was further implemented on an existing BCI system platform, called the BCI2000 system. The algorithm performance was evaluated using an existing reliable database provided by BCI Competition 2003. Numerical experimental results produced by the database indicated that the proposed algorithm actually achieved 100% character recognition accuracy.

A signal-processing-based technique for P300 evoked potential detection with the applications into automated character recognition