2D VIRTUAL CURSOR CONTROL USING EEG BASED BRAIN COMPUTER INTERFACE

Abstract

This paper focuses on a Brain Computer Interface (BCI) system that allows for cursor control on a two-dimensional plane through Electroencephalography (EEG) indicators. Mu/Beta rhythms are associated with horizontal movement and P300 potentials are associated with the vertical movement. Existing classification techniques on EEG datasets are employed and K-means clustering is used for classification and big data integration tools are used to integrate the results into a Python-based graphical user interface (GUI). Even if there is signal noise as a limitation, the proposed method shows applicability for assistive technologies for motor disabled people. For future work, the recommendations are to fine tune the approach and consider real-world applications.