Abstract:
A brain-computer interface (BCI) is a system that provides direct communication between cognitive activity and external systems. This project involved creating a BCI that allowed participants to learn how to control virtual reality using only their brain waves. The objective was to devise a novel BCI system that employed machine learning algorithms to recognize brain activity associated with imagined movements and repurpose this information to navigate through a virtual environment. Techniques used to accomplish this task included applying existing linear classifiers, such as Support Vector Machines (SVMs) and Principal Components Analysis (PCA), to frequency domain representations of the data. On average, 87% of the test data points were classified accurately, proving that motor imagery has the potential to be utilized as an intelligent controller.
