A Brain-Computer Interface Method Combined with Eye Tracking for 3-D Interaction
이의철
|
J NEUROSCI METH
190/2
(2010)
With the recent increase in the number of three-dimensional (3-D) applications, the need for interfaces to the 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 a BCI and eye tracking are combined to analyze depth navigation including selection and two-dimensional (2-D) gaze direction, respectively. The proposed method is novel in the following five ways compared to previous works. First, a device to measure both 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 EEG power spectrum. Third, depth control for the 3-D interaction interface is implemented by the imaginary movement of arm reaching. Fourth, a selection method is implemented by the imaginary movement of hand grabbing. And fifth, for the independent operation of gazing and a BCI, a mode selection method is proposed that measures a user’s concentration by analyzing 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 3-D interaction method using eye tracking and a BCI.
- 초록
With the recent increase in the number of three-dimensional (3-D) applications, the need for interfaces to the
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 a BCI and eye tracking are combined to analyze depth
navigation including selection and two-dimensional (2-D) gaze direction, respectively.
The proposed method is novel in the following five ways compared to previous works. First, a device to measure both
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
EEG power spectrum. Third, depth control for the 3-D interaction interface is implemented by the imaginary movement
of arm reaching. Fourth, a selection method is implemented by the imaginary movement of hand grabbing. And fifth, for
the independent operation of gazing and a BCI, a mode selection method is proposed that measures a user’s
concentration by analyzing 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 3-D interaction method using eye
tracking and a BCI.
- 초록
With the recent increase in the number of three-dimensional (3-D) applications, the need for interfaces to the
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 a BCI and eye tracking are combined to analyze depth
navigation including selection and two-dimensional (2-D) gaze direction, respectively.
The proposed method is novel in the following five ways compared to previous works. First, a device to measure both
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
EEG power spectrum. Third, depth control for the 3-D interaction interface is implemented by the imaginary movement
of arm reaching. Fourth, a selection method is implemented by the imaginary movement of hand grabbing. And fifth, for
the independent operation of gazing and a BCI, a mode selection method is proposed that measures a user’s
concentration by analyzing 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 3-D interaction method using eye
tracking and a BCI.
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