Real-time EEG based object recognition system using Brain Computer Interface

A Brain Computer Interface (BCI) provides a communication path between human brain and the computer system. The major goal of BCI research is to develop a system that allows disabled people to communicate with other people, to control artificial limbs, or to control their environment. BCI is a challenging topic of computer vision research. It is extensively used by disabled people to communicate with other persons and helps to interact with the external environments. This paper provides an insight into object recognition by analyzing EEG signals in real-time. Three machine learning algorithms are implemented which are used for classification by supervised learning, namely Decision Trees, K-Nearest Neighbors and Support Vector Machine (SVM), multiple training sets and users are taken into account during the experiment and the efficiency of each algorithm is compared to suggest the best suited algorithm for this purpose.