A METHOD FOR EXTRACTING MEANINGFUL SIGNALS FROM EVENT RELATED POTENTIALS

摘要

In this paper we propose a method for detecting signals from experimental data such that their signal-to-noise ratios are less than 1. The method uses the feature of data distribution. Then we apply the method to the detection of event-related-potentials (ERPs) or P3 signals (or P300 signals). A P3 signal appears about 300ms later after a stimulus is given, where the P3 signal is in an Electroencephalogram (EEG) and its S/N is less than 1. A simple signal-averaging procedure has been usually used to extract P3 signals or ERPs from EEGs. We discuss the problem of how we can observe the change of brain activities from the change of P3 signals obtained by the proposed method.