Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard

Uşaklı, Ali Bülent; Gürkan, Serkan

doi:10.1109/tim.2009.2030923

Cited by 152 publications

(80 citation statements)

References 21 publications

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“…In this way, eye movements can be used as control signals for human-machine interfaces, allowing for interaction with the environment. This offers a potentially useful interface for disabled individuals [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors

Belkacem

Shin

Kambara

et al. 2015

Biomedical Signal Processing and Control

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Section: Introductionmentioning

confidence: 99%

Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors

Belkacem

Shin

Kambara

et al. 2015

Biomedical Signal Processing and Control

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“…EOG has some advantages over other techniques of eyetracking, since it does not limit the field of vision and is more affordable than other types of eyetracking techniques. These benefits may make it an effective tool to use instead of EEG signals for researchers in the domain of braincomputer interface (Nakanishi, Mitsukara, Wang, Wang, & Jung, 2012;Usakli & Gurkan, 2010). Along with these studies, efforts are being made to develop algorithms for the automatic analysis of EOG data (Pettersson et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Qualitative modeling of the decision-making process using electrooculography

Marandi

Sabzpoushan

2014

Behav Res

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A novel method based on electrooculography (EOG) has been introduced in this work to study the decision-making process. An experiment was designed and implemented wherein subjects were asked to choose between two items from the same category that were presented within a limited time. The EOG and voice signals of the subjects were recorded during the experiment. A calibration task was performed to map the EOG signals to their corresponding gaze positions on the screen by using an artificial neural network. To analyze the data, 16 parameters were extracted from the response time and EOG signals of the subjects. Evaluation and comparison of the parameters, together with subjects' choices, revealed functional information. On the basis of this information, subjects switched their eye gazes between items about three times on average. We also found, according to statistical hypothesis testing-that is, a t test, t(10) = 71.62, SE = 1.25, p < .0001-that the correspondence rate of a subjects' gaze at the moment of selection with the selected item was significant. Ultimately, on the basis of these results, we propose a qualitative choice model for the decision-making task.Keywords Decision making . Eye movements . Electrooculography . Neuromarketing Decision making can be regarded as a cognitive process of the evaluation and selection of a course of action from among various alternatives, through which a final choice is produced.Developing novel technologies for research on human behavior during complex cognitive processes such as decision making is drawing more attention nowadays.Among other novel technologies, eyetracking techniques have been developed over recent decades as a window into people's visual and cognitive processes. Eyetracking techniques have been applied to realms of behavior research such as image scanning (e.g

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“…Matrix , (Arif and Stuerzlinger, 2013), (Bhattacharya and Laha, 2012), (Burgbacher and Hinrichs, 2014), , (Faraj et al, 2009a), (Faraj et al, 2009a), (Faraj et al, 2009b), (Francis and Johnson, 2011), (Gelormini and Bishop, 2013), , (Ghosh, 2011), (Gizatdinova et al, 2012a), (Gizatdinova et al, 2012b), (Guerrier et al, 2011), (Guerrier et al, 2013), (Guerrier et al, 2013), (Jain and Bhattacharya, 2010), (Janpinijrut et al, 2011b), (Kim et al, 2014), (Klima and Slovacek, 2009), (Kwon et al, 2009), (Merino et al, 2010), (Merino et al, 2012), (Miró-Borrás and Bernabeu-Soler, 2009), (Nicolau et al, 2013), (Panwar et al, 2012), (Phanchaipetch and Nattee, 2012), (Polácek et al, 2012), (Sarafis and Markoulidis, 2010), , (Sharma et al, 2012), (Simathamanand and Piromsopa, 2011), (Truong et al, 2013), (Usakli and Gurkan, 2010), (Varcholik et al, 2012), (Vella and Vigouroux, 2013), (Wu et al, 2014), (Yang et al, 2013) Circular (Prabhu and Prasad, 2011), (Topal et al, 2012) Distribution of symbols Frequency (Bhattacharya and Laha, 2012), (Faraj et al, 2009c), (Francis ...…”

Section: Positioning Of Keysmentioning

confidence: 99%

“…Yes , (Arif and Stuerzlinger, 2013), (Corley et al, 2012), (Francis and Johnson, 2011), (Gelormini and Bishop, 2013), (Ghosh, 2011), (Guerrier et al, 2013), (Janpinijrut et al, 2011b), (Kim et al, 2014), (Klima and Slovacek, 2009), (Merino et al, 2010), (Millet et al, 2009), (Nicolau et al, 2013), (Prabhu and Prasad, 2011), (Sharma et al, 2012), (Simathamanand and Piromsopa, 2011), (Usakli and Gurkan, 2010), (Vella and Vigouroux, 2013), (Wu et al, 2014) No , (Arif and Stuerzlinger, 2013), (Arif and Stuerzlinger, 2013), (Bhattacharya and Laha, 2012), (Burgbacher and Hinrichs, 2014), (Corley et al, 2012), (Eklund et al, 2010), (Faraj et al, 2009c), (Faraj et al, 2009a), (Faraj et al, 2009b), (Francis and Johnson, 2011), (Gelormini and Bishop, 2013), (Ghosh, 2011), (Gizatdinova et al, 2012a), (Gizatdinova et al, 2012b), (Guerrier et al, 2013), (Jain and Bhattacharya, 2010), (Janpinijrut et al, 2011a), (Kim et al, 2014), (Klima and Slovacek, 2009), (Merino et al, 2010), (Millet et al, 2009), (Miró-Borrás and Bernabeu-S...…”

Section: Display Special Charactersmentioning

confidence: 99%

A new concept of assistive virtual keyboards based on a systematic review of text entry optimization techniques

et al. 2016

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Introduction: Due to the increasing popularization of computers and the internet expansion, Alternative and Augmentative Communication technologies have been employed to restore the ability to communicate of people with aphasia and tetraplegia. Virtual keyboards are one of the most primitive mechanisms for alternatively entering text and play a very important role in accomplishing this task. However, the text entry for this kind of keyboard is much slower than entering information through their physical counterparts. Many techniques and layouts have been proposed to improve the typing performance of virtual keyboards, each one concerning a different issue or solving a specific problem. However, not all of them are suitable to assist seriously people with motor impairment. Methods: In order to develop an assistive virtual keyboard with improved typing performance, we performed a systematic review on scientific databases. Results: We found 250 related papers and 52 of them were selected to compose. After that, we identified eight essentials virtual keyboard features, five methods to optimize data entry performance and five metrics to assess typing performance. Conclusion: Based on this review, we introduce a concept of an assistive, optimized, compact and adaptive virtual keyboard that gathers a set of suitable techniques such as: a new ambiguous keyboard layout, disambiguation algorithms, dynamic scan techniques, static text prediction of letters and words and, finally, the use of phonetic and similarity algorithms to reduce the user's typing error rate.

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Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard

Cited by 152 publications

References 21 publications

Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors

Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors

Qualitative modeling of the decision-making process using electrooculography

A new concept of assistive virtual keyboards based on a systematic review of text entry optimization techniques

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