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Sensors 2013, 13(3), 3454-3472; doi:10.3390/s130303454

Enhanced Perception of User Intention by Combining EEG and Gaze-Tracking for Brain-Computer Interfaces (BCIs)

1
Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea
2
Division of Digital Media Technology, Sangmyung University, 7 Hongji-dong, Jongno-gu, Seoul 110-743, Korea
*
Author to whom correspondence should be addressed.
Received: 4 January 2013 / Revised: 13 February 2013 / Accepted: 8 March 2013 / Published: 13 March 2013
(This article belongs to the Section Physical Sensors)
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Abstract

Speller UI systems tend to be less accurate because of individual variation and the noise of EEG signals. Therefore, we propose a new method to combine the EEG signals and gaze-tracking. This research is novel in the following four aspects. First, two wearable devices are combined to simultaneously measure both the EEG signal and the gaze position. Second, the speller UI system usually has a 6 × 6 matrix of alphanumeric characters, which has disadvantage in that the number of characters is limited to 36. Thus, a 12 × 12 matrix that includes 144 characters is used. Third, in order to reduce the highlighting time of each of the 12 × 12 rows and columns, only the three rows and three columns (which are determined on the basis of the 3 × 3 area centered on the user’s gaze position) are highlighted. Fourth, by analyzing the P300 EEG signal that is obtained only when each of the 3 × 3 rows and columns is highlighted, the accuracy of selecting the correct character is enhanced. The experimental results showed that the accuracy of proposed method was higher than the other methods.
Keywords: EEG signal; gaze-tracking; two wearable devices; speller UI system EEG signal; gaze-tracking; two wearable devices; speller UI system
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Choi, J.-S.; Bang, J.W.; Park, K.R.; Whang, M. Enhanced Perception of User Intention by Combining EEG and Gaze-Tracking for Brain-Computer Interfaces (BCIs). Sensors 2013, 13, 3454-3472.

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