Density based support vector machine classification for a synchronous EEG path tracing virtual training environment

The use of Brain-Computer Interface (BCI) has been increasing exponentially in the recent years due to the use of low-cost commercial Fast Fourier Transform (FFT) based EEG reading devices with non-clinical accuracy for consumer application development. Also, the design and implementation of 3D virtual environments for BCI training purposes has proven to be effective due to the high interaction with the end user and the assistance for recreating a specific type of signal or behavior. The aim of this paper is to present a method and the results of applying a binary Density Based Support Vector Machine (DBSVM) Classifier in a 3D virtual environment designed for interacting with EEG predefined signal patterns. The environment trains the classifier by taking 180 second EPOCHs and classifying them into a successful/unsuccessful attempt per test subject. The applications can be extended for implementing a mind-wave pattern password or tracing a specific set of mind-based commands for virtual path tracing purposes. The tested SVM had a success rate of 60%. Further work includes the study of different classifier features and implementation of a dynamic classifier.