Division of Fusion and Convergence of Mathematical Sciences, National Institute for Mathematical Sciences (NIMS),Yuseong-gu, Daejeon 305-340, Republic of Korea.
J Neurosci Methods. 2010 Jul 15;190(2):289-98. doi: 10.1016/j.jneumeth.2010.05.008. Epub 2010 May 16.
With the recent increase in the number of three-dimensional (3D) applications, the need for interfaces to these applications has increased. Although the eye tracking method has been widely used as an interaction interface for hand-disabled persons, this approach cannot be used for depth directional navigation. To solve this problem, we propose a new brain computer interface (BCI) method in which the BCI and eye tracking are combined to analyze depth navigation, including selection and two-dimensional (2D) gaze direction, respectively. The proposed method is novel in the following five ways compared to previous works. First, a device to measure both the gaze direction and an electroencephalogram (EEG) pattern is proposed with the sensors needed to measure the EEG attached to a head-mounted eye tracking device. Second, the reliability of the BCI interface is verified by demonstrating that there is no difference between the real and the imaginary movements for the same work in terms of the EEG power spectrum. Third, depth control for the 3D interaction interface is implemented by an imaginary arm reaching movement. Fourth, a selection method is implemented by an imaginary hand grabbing movement. Finally, for the independent operation of gazing and the BCI, a mode selection method is proposed that measures a user's concentration by analyzing the pupil accommodation speed, which is not affected by the operation of gazing and the BCI. According to experimental results, we confirmed the feasibility of the proposed 3D interaction method using eye tracking and a BCI.
随着三维 (3D) 应用数量的增加，对这些应用的接口的需求也在增加。虽然眼动跟踪方法已被广泛用作手部残疾人士的交互界面，但这种方法无法用于深度方向导航。为了解决这个问题，我们提出了一种新的脑机接口 (BCI) 方法，将 BCI 和眼动跟踪结合起来，分别分析深度导航中的选择和二维 (2D) 注视方向。与以前的工作相比，该方法在以下五个方面具有创新性。首先，提出了一种同时测量注视方向和脑电图 (EEG) 模式的设备，该设备将测量 EEG 所需的传感器附加到头戴式眼动跟踪设备上。其次，通过证明对于相同的工作，真实运动和想象运动在 EEG 功率谱方面没有差异，验证了 BCI 接口的可靠性。第三，通过想象手臂的伸展运动来实现 3D 交互界面的深度控制。第四，通过想象手抓取运动来实现选择方法。最后，为了实现注视和 BCI 的独立操作，提出了一种通过分析瞳孔调节速度来测量用户注意力的模式选择方法，该方法不受注视和 BCI 操作的影响。根据实验结果，我们验证了使用眼动跟踪和 BCI 进行 3D 交互方法的可行性。
