Abstract
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°, 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.
Original language | English |
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Pages (from-to) | 581-588 |
Number of pages | 8 |
Journal | Journal of Medical and Biological Engineering |
Volume | 34 |
Issue number | 6 |
Publication status | Published - 2014 |
Externally published | Yes |
Keywords
- Brain-computer interface (BCI)
- Electroencephalography (EEG)
- Electrooculography (EOG)
- Eye movements
- Visual angle
ASJC Scopus subject areas
- Biomedical Engineering