Classification of Four Eye Directions from EEG Signals for Eye-Movement-Based Communication Systems

Many classification algorithms have been developed to distinguish brain activity states during different mental tasks. Although these algorithms achieve good results, they require many training loops to make a decision. As the complexity of an algorithm grows, it becomes more and more difficult to execute commands in real time. The detection of eye movement from brain activity data provides a new means of communication and device control for disabled and healthy people. This paper proposes a simple algorithm for offline recognition of four directions of eye movement from electroencephalographic (EEG) signals. A hierarchical classification algorithm is developed using a thresholding method. A strategy without a prior model is used to distinguish the four cardinal directions and a single trial is used to make a decision. Using a visual angle of 5 degrees, the results suggest that EEG signals are feasible and useful for detecting eye movements. The proposed algorithm was efficient in the classification phase with an obtained accuracy of 50-85% for twenty subjects.