EOG Pattern Recognition Trial for a Human Computer Interface

Brunner, Stefanie; Hanke, Sten; Wassertheuer, Siegfried; Hochgatterer, Andreas

doi:10.1007/978-3-540-73281-5_84

Cited by 13 publications

(6 citation statements)

References 1 publication

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“…In order to develop all of these HCIs, various techniques have been proposed, which can be divided into two main types: pattern recognition and non-pattern recognition. In pattern recognition, features extracted are discriminated by a suitable classifier (Brunner et al, 2007). Time-domain features that have been frequently used are mean value, peak duration, peak polarity and slope (Kherlopain et al, 2006).…”

Section: Previous Researchmentioning

confidence: 99%

Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements

Aungsakun¹

2012

Int. J. Phys. Sci.

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Electrooculography (EOG) signal is one of the useful electro-physiological signals. The EOG signals provide information about eye movements that can be used as a control signal in human-computer interface (HCI). Usually, eight-directional movements, including up, down, right, left, upright , up-left, downright and down-left, are proposed. Development of the EOG signal classification has been shown more increasing interest in the last decade; however, the effect of noises on classification system is a major problem to degrade the usefulness of EOG-based HCI. A robust classification algorithm of the eight movements is proposed, in which this technique can conduct the effect of noises in EOG signal, particularly for involuntary movements and eye-blink artifacts. The proposed algorithm was based on the onset analysis, feature extraction, the first derivative technique and threshold classification. Eight beneficial time domain features were proposed including the peak and the valley amplitude positions, and the upper and the lower wavelengths of two EOG channels, vertical and horizontal channels. Based on the optimal threshold values and conditions, the results showed that classification accuracy reached 100% for three-subject testing. In addition, the first derivative technique was additionally implemented in order to avoid the eye-blink artifact and other eight time domain features, that is, peak amplitude and area under curve, have been investigated for use in advanced HCI interfaces, notably, eye activity and eye writing recognitions.

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Section: Previous Researchmentioning

confidence: 99%

Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements

Aungsakun¹

2012

Int. J. Phys. Sci.

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“…Various techniques may be used to model the ocular motor system using EOG and detect eye movements. These include saccadic eye-movement quantification [ 16 ], pattern recognition [ 17 ], spectral analysis [ 18 ], peak detection deterministic finite automata [ 19 ], multiple feature classification [ 20 ], the Kalman filter [ 21 ], neural networks [ 22 – 25 ] and the support vector machine [ 26 ]. Notable efforts have also been made to reduce and eliminate the problems associated with gaze detection in EOG, such as drift, blink, overshoot, ripple and jitter [ 12 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recent new research has focused on using electrooculograms to create efficient HCIs [ 18 , 28 , 29 ] and developing novel electrode configurations to produce wearable EOG recording systems, such as wearable headphone-type gaze detectors [ 21 ], wearable EOG goggles [ 17 , 30 , 31 ], or light-weight head caps [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

Barea

Boquete

Rodríguez-Ascariz

et al. 2010

Sensors

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This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes.

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“…The ocular movement records have been widely used in processing and classification of biological signals and pathological conditions: clinical sleep scoring [1], cerebellar dysfunctions [2,3], diagnosis of the visual system [4,5], among others, also in human computer interface, and visualguided devices [6][7][8]. The spino cerebellar ataxia type 2 (SCA-2) is an autosomal dominant cerebellar hereditary ataxia with the highest prevalence in Cuba, reporting up to 43 cases per 100,000 inhabitants in the province of Holguin.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis of Ataxia SCA-2 Using a Blind Source Separation Algorithm

et al. 2010

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This work discusses a new approach for ataxia SCA-2 diagnosis based on the application of independent component analysis to the data obtained by electro-oculography in several experiments carried out over healthy and sick subjects. Abnormalities in the oculomotor system are well-known clinical symptoms in patients of several neurodegenerative diseases, including modifications in latency, peak velocity, and deviation in saccadic movements, causing changes in the waveform of the patient response. The changes in the morphology waveform suggest a higher degree of statistic independence in sick patients when compared to healthy individuals regarding the patient response to the visual saccadic stimulus modeled by means of digital generated saccade waveforms. The electro-oculogram records of thirteen patients diagnosed with ataxia SCA2 (a neurodegenerative hereditary disease) and thirteen healthy subjects used as control were processed to extract saccades.

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EOG Pattern Recognition Trial for a Human Computer Interface

Cited by 13 publications

References 1 publication

Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements

Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements

Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

Computer-Aided Diagnosis of Ataxia SCA-2 Using a Blind Source Separation Algorithm

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