Title :
Controlling a Human–Computer Interface System With a Novel Classification Method that Uses Electrooculography Signals
Author :
Shang-Lin Wu ; Lun-De Liao ; Shao-Wei Lu ; Wei-Ling Jiang ; Shi-An Chen ; Chin-Teng Lin
Author_Institution :
Inst. of Electr. Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
Abstract :
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.
Keywords :
biomechanics; brain-computer interfaces; electro-oculography; eye; feature extraction; medical signal processing; signal classification; EOG; HCI; electrooculography signals; eye movements; feature extraction; human-computer interface system control; signal acquisition; signal classification; wireless EOG-based HCI device; Classification algorithms; Electrodes; Electrooculography; Feature extraction; Human computer interaction; Noise; Wireless communication; Biosignal processing; classification methods; electrooculography (EOG); eye movement detection; human–computer interface (HCI); Brain-Computer Interfaces; Electrodes; Electrooculography; Equipment Design; Equipment Failure Analysis; Eye Movements; Humans; Man-Machine Systems; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Telemetry;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2013.2248154