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Detection of eye movements and eye blinks using a portable two-channel EEG platform
Xingyi Zhong1, Guangye Li1, Ce Xu1
1State Key Laboratory of Mechanical Systems and Vibrations, Shanghai Jiao Tong University, Shanghai, China.
Background:
The ability to detect eye movements can facilitate human-computer interaction (HCI) and may complement brain-computer interfaces (BCIs). Recent studies have shown that multi-channel EEG systems can provide information about eye movements, but these systems can be bulky and/or require complex setup.
New Method:
We introduce a portable, two-channel EEG platform that can be placed in seconds and detect eye blinks/movements and gaze trajectories. Forty adults performed cued blinks and horizontal/vertical gaze shifts; 21 EEG features were extracted, and machine learning models were evaluated with leave-one-subject-out validation.
Results:
Our system effectively identified eye blinks (avg. detection accuracy of 95%, 50% chance) and horizontal eye movements (avg. accuracy of 94%, 33% chance), and showed decreased performance detecting vertical eye movements (avg. accuracy of 60%, 33% chance). It was also able to predict horizontal and vertical eye movement trajectories (r = 0.79 and r = 0.14, respectively).
Comparison With Existing Methods:
Classification accuracies for eye blinks and horizontal eye movements using our system with only two electrodes are comparable to those previously reported only for complex multi-channel EEG/EOG setups.
Conclusion:
This study provides evidence, for the first time, that a wearable EEG device can give substantial information about eye blinks and eye movements. With further refinements, this approach may enable portable solutions for real-world HCI and BCI applications.
