A resonant eye-tracking microsystem for velocity estimation of saccades and foveated rendering

Sarkar, Niladri; O'Hanlon, B.; Rohani, Arash; Strathearn, D.; Lee, G.; Olfat, M.; Mansour, Raafat R.

doi:10.1109/memsys.2017.7863402

Cited by 12 publications

(2 citation statements)

References 5 publications

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“…ese erroneous and unconscious EEG signals will bring difficulties to feature extraction and classification, resulting in a low recognition rate [36,37].…”

Section: Discussionmentioning

confidence: 99%

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

Shao

Zhang

Belkacem

et al. 2020

Journal of Healthcare Engineering

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The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots have rapidly developed and have gradually been applied to enhance the quality of people’s lives. We focus on connecting the brain with a mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer the promise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomy, mobility, and abilities. Several types of robots have been controlled using BCI systems to complete real-time simple and/or complicated tasks with high performances. In this paper, a new EEG-based intelligent teleoperation system was designed for a mobile wall-crawling cleaning robot. This robot uses crawler type instead of the traditional wheel type to be used for window or floor cleaning. For EEG-based system controlling the robot position to climb the wall and complete the tasks of cleaning, we extracted steady state visually evoked potential (SSVEP) from the collected electroencephalography (EEG) signal. The visual stimulation interface in the proposed SSVEP-based BCI was composed of four flicker pieces with different frequencies (e.g., 6 Hz, 7.5 Hz, 8.57 Hz, and 10 Hz). Seven subjects were able to smoothly control the movement directions of the cleaning robot by looking at the corresponding flicker using their brain activity. To solve the multiclass problem, thereby achieving the purpose of cleaning the wall within a short period, the canonical correlation analysis (CCA) classification algorithm had been used. Offline and online experiments were held to analyze/classify EEG signals and use them as real-time commands. The proposed system was efficient in the classification and control phases with an obtained accuracy of 89.92% and had an efficient response speed and timing with a bit rate of 22.23 bits/min. These results suggested that the proposed EEG-based clean robot system is promising for smart home control in terms of completing the tasks of cleaning the walls with efficiency, safety, and robustness.

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“…ese erroneous and unconscious EEG signals will bring difficulties to feature extraction and classification, resulting in a low recognition rate [36,37].…”

Section: Discussionmentioning

confidence: 99%

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

Shao

Zhang

Belkacem

et al. 2020

Journal of Healthcare Engineering

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“…One of alternative optical detection methods using the signal reflected from the eyeball is MEMS -mirror scanning method [13][14][15][16]. These systems work based on MEMS movable mirrors scanning the area of the eyeball along a specific curve.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Pomianek,

Piszczek,

Maciejewski

2021

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Eye tracking systems are mostly video-based methods which require significant computation to achieve good accuracy. An alternative method with comparable accuracy but less computational expense is 2D microelectromechanical (MEMS) mirror scanning. However, this technology is relatively new and there are not many publications on it. The purpose of this study was to examine how individual parameters of system components can affect the accuracy of pupil position estimation. The study was conducted based on a virtual simulator. It was shown that the optimal detector field of view (FOV) depends on the frequency ratio of the MEMS mirror axis. For a value of 1:13, the smallest errors were at 0.1 • , 1.65 • , 2.3 • , and 2.95 • . The error for the impact of the signal sampling rate above 3 kHz stabilizes at 0.065 • and no longer changes its value regardless of increasing the number of samples. The error for the frequency ratio of the MEMS mirror axis increases linearly in the range of 0.065 • -0.1 • up to the ratio of 1:230. Above this there is a sudden increase to the average value of 0.3 • . The conducted research provides guidance in the selection of parameters for the construction of eye tracking MEMS mirror-based systems.

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A Novel Camera-Free Eye Tracking Sensor for Augmented Reality Based on Laser Scanning

et al. 2020

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A resonant eye-tracking microsystem for velocity estimation of saccades and foveated rendering

Cited by 12 publications

References 5 publications

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

Metrology and Measurement Systems

A Novel Camera-Free Eye Tracking Sensor for Augmented Reality Based on Laser Scanning

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