Hsieh Chi-Hsuan, Huang Yuan-Hao
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:7238-41. doi: 10.1109/EMBC.2015.7320062.
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.
本文提出了一种用于基于脑电图的脑机接口的眼动分类算法。所提出的系统首先使用低复杂度的扩展移动差分滤波器来获取眼动事件的纯净脉冲波形。然后，设计了一种脉宽解调算法来识别左/右/上/下方向的眼动事件。通过排除脉宽较小的脉冲，可以轻松消除眨眼事件，从而提高检测率。此外，脉宽解调仅需加法运算即可实现近90%的平均检测率。其计算复杂度远低于文献中的其他研究成果。
