A single channel-single trial P300 detection algorithm

A Brain Computer Interface (BCI) system allows the users to communicate with their surroundings without using any muscle activity. Many of these systems are based on the analysis of Event Related Potentials (ERPs) such as P300. P300 speller is one of the common BCI systems which attract a lot of attention; however, there are still a lot of flaws in these systems which should be considered. Since ERPs such as P300 signals have a very low Signal to Noise Ratio (SNR), single trial analysis of these signals is difficult and in many papers, denoising methods such as synchronous averaging were proposed to reduce random noise; however, it reduces the communication rate greatly. Another major problem in many BCI applications is the numerous number of channels needed to record EEG signals in order to have a reliable system. In this paper, a new method is presented to detect P300 signals through single channel data analysis and also it reaches an average accuracy of 65% in single trial P300 detection.