title: Low-complexity EEG-based eye movement classification using extended moving difference filter and pulse width demodulation.
abstract: This paper presents an eye movement classification algorithm for EEG-based brain-computer interface. The proposed system first used a low-complexity extended moving difference filter to acquire clean pulse waveform of eye-movement events. Then, a pulse width demodulation algorithm was designed to identify eye-movement events of left/right/up/down directions. The eye blinking events can be easily eliminated by excluding the pulses with small pulse-width, and thus the detection rate can be improved. Besides, the pulse width demodulation requires only addition operations to achieve a near 90% averaged detection. The computation complexity is much lower than those of other works in the literature.
authors.forename: Chi-Hsuan
authors.initials: CH
authors.lastname: Hsieh
authors.forename: Yuan-Hao
authors.initials: YH
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001767:Blinking; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005133:Eye Movements; D006801:Humans; D008991:Monitoring, Physiologic; D012815:Signal Processing, Computer-Assisted