Character identification by maximizing the difference between target and non-target responses in EEG without sophisticated classifiers

We propose a simple character identification method demonstrated by using an electroencephalogram (EEG) with a stimulus presentation technique. The method assigns a code maximizing the minimum Hamming distance between character codes. Character identification is achieved by increasing the difference between target and non-target responses without sophisticated classifiers such as neural network or support vector machine. Here, we introduce two kinds of scores reflecting the existence of the P300 component from the point of time and frequency domains. We then applied this method to character identification using a 3×3 matrix and compared the results to that of a conventional P300 speller. The accuracy of character identification with our method indicated a performance of 100% character identification from five subjects. In contrast, the correct character was detected in two subjects and a wrong one was detected for one subject. For the remaining two subjects, no character was detected within ten trials. Our method required 4.8 trials on average to detect the correct character.