An Accurate and Inter-Operatable Fuzzy-Based System Using Genetic and Canonical Correlation Analysis Methods in Internet-of-Brain Things

Abstract

The brain computer interface is defined as the way of acquiring the brain signals that analyse and translates them into commands that are relayed to intelligent devices for carrying out various actions. Through number of BCI mechanism and approaches have been proposed by various scientists to empower the individuals for directly controlling their objects via their thoughts. However, the actual implementation and realization of this method faces number of challenging with low accuracy and less interoperability. In addition, the pre-processing signals and feature extraction process is further time consuming and less accurate. In order to overcome the mentioned issues, this paper proposes an accurate and highly inter-operable system using genetic fuzzy system along. The predictive model and analysis can be further improved using canonical correlation analysis. The proposed framework is validated and demonstrated using brain typing system analysis. The results are computed against accuracy, latency and interoperability of the signals received from brain with less SNR along with traditional method. The proposed mechanism shows approximately 87% improvement as compare to existing approaches during the simulation over various performance metrics.