Controlling a Human-Computer Interface System With a Novel Classification Method that Uses Electrooculography Signals

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no e...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2013-08, Vol.60 (8), p.2133-2141
Main Authors: Wu, Shang-Lin, Liao, Lun-De, Lu, Shao-Wei, Jiang, Wei-Ling, Chen, Shi-An, Lin, Chin-Teng
Format: Article
Language:English
Subjects:
Biosignal processing
Brain-Computer Interfaces
Classification
Classification algorithms
classification methods
Electrodes
Electrooculography
electrooculography (EOG)
Electrooculography - instrumentation
Equipment Design
Equipment Failure Analysis
eye movement detection
Eye movements
Eye Movements - physiology
Feature extraction
Human computer interaction
human-computer interface (HCI)
Humans
Man-Machine Systems
Noise
Pattern Recognition, Automated - methods
Signal Processing, Computer-Assisted - instrumentation
Studies
Telemetry - instrumentation
Wireless communication
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Summary:Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2013.2248154