P300 Speller with Enhanced Accuracy in Brain Computer Interface

Communication channel connecting the brain to a computer or other electronic devices is nothing but brain computer interface (BCI). The information extraction from brain is a very demanding task. Brain signals are mixed with other signals or noises coming from a finite set of brain activities that overlap in both time and space. Electroencephalogram (EEG) has been the most popular signal that is used in developing BCI systems because of its low cost and convenience of use. Such systems may provide a complementary or alternative way for individuals with severe neuromuscular disorders to drive applications to extend their scope of mobility and, thus, improve their quality of life and independence. P300 wave is an event related potential which evoked in the process of decision making of human brain which can be generated using oddball paradigm. In this paper we are discussing a system for improving accuracy in P300 potentials. The main idea is to capture the signals from the brain for P300 BCI and after processing signals desired output can get with higher accuracy. The accuracy can be increased by filtering and classification algorithms such as Linear Discriminant Algorithm (LDA) and special filters.