Coughlin M J, Cutmore T R H, Hine T J
School of Applied Psychology (Health Sciences), Mt Gravatt Campus, Griffith University, Brisbane, Queensland 4111, Australia.
Comput Methods Programs Biomed. 2004 Dec;76(3):207-20. doi: 10.1016/j.cmpb.2004.06.001.
The electro-oculogram (EOG) continues to be widely used to record eye movements especially in clinical settings. However, an efficient and accurate means of converting these recordings into eye position is lacking. An artificial neural network (ANN) that maps two-dimensional (2D) eye movement recordings into 2D eye positions can enhance the utility of such recordings. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33 degrees . Linear perceptrons (LPs) were only nearly half as accurate. For five subjects, the mean accuracy provided by the MLPs was 1.09 degrees of visual angle ( degrees ) for EOG data, and 0.98 degrees for an infrared eye tracker. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different from that obtained with the infrared tracker. Using initial weights trained on another person reduced MLP training time, reaching convergence in as little as 20 iterations.
眼电图（EOG）仍然被广泛用于记录眼球运动，尤其是在临床环境中。然而，目前缺乏一种将这些记录转换为眼球位置的高效且准确的方法。一种将二维（2D）眼球运动记录映射到二维眼球位置的人工神经网络（ANN）可以提高此类记录的实用性。具有非线性激活函数并通过反向传播进行训练的多层感知器（MLP）被证明能够将模拟眼电图数据校准到平均精度为0.33度。线性感知器（LP）的精度仅为其近一半。对于五名受试者，多层感知器对眼电图数据提供的平均精度为1.09视角度（°），对红外眼动仪为0.98度。多层感知器能够将二维扫视眼电图校准到与红外跟踪器获得的精度无显著差异的精度。使用在另一个人身上训练的初始权重减少了多层感知器的训练时间，在短短20次迭代中就达到了收敛。
