Browsing by Author "Gupta, Akshansh"

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EOG-Based Eye Movement Classification and Application on HCI Baseball Game
(2019-01-01)
Lin, Chin-Teng
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King, Juang-Tai
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Bharadwaj, Priyanka
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Chen, Chih-Hao
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Gupta, Akshansh
Electrooculography (EOG) is considered as the most stable physiological signal in the development of human-computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.
Voice Navigation Effects on Real-World Lane Change Driving Analysis Using an Electroencephalogram
(2018-01-01)
Lin, Chin-Teng
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King, Jung-Tai
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Singh, Avinash Kumar
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Gupta, Akshansh
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Ma, Zhenyuan
Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving. In this paper, a real-world experiment is designed to acquire the electroencephalography (EEG) and in-car information to analyze the dynamic brain activity while the driver is performing the lane-changing task based on the auditory instructions from an in-car navigation system. The results show that auditory cues can influence the speed and increase the frontal EEG delta and beta power, which is related to motor preparation and decision making during a lane change. However, there were no significant results on the alpha power. A better lane-change assessment can be obtained using specific vehicle information (lateral acceleration and heading angle) with EEG features for future naturalized driving study.