Algorithm for automatic analysis of electro-oculographic data

Pettersson, Kati; Jagadeesan, Sharman; Lukander, Kristian; Henelius, Andreas; Hæggström, Edward; Müller, Kiti

doi:10.1186/1475-925x-12-110

Cited by 45 publications

(43 citation statements)

References 43 publications

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“…• recordings on healthy subjects [22] and .80 of recall on subjects with Obstructive Sleep Apnea Syndrome (OSAS) [15]. Other related research conducted by Tigges et al shows an accuracy of .92 [28].…”

Section: Resultsmentioning

confidence: 99%

“…Exists several methods and algorithms for identifying saccades in electrooculograms, the vast majority of them based on kinetic thresholds [11,14,31,26], using suppervised learning [28,6], unsupervised learning [20] or other novel approachs [18,22] like particle filters [8]. These methods were designed to work in a not constrained scheme having advantages in a lot of scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Non Spontaneous Saccadic Movements Identification in Clinical Electrooculography Using Machine Learning

Becerra-García

García−Bermúdez

Joya-Caparrós

et al. 2015

Advances in Computational Intelligence

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Abstract. In this paper we evaluate the use of the machine learning algorithms Support Vector Machines, K-Nearest Neighbors, CART decision trees and Naive Bayes to identify non spontaneous saccades in clinical electrooculography tests. Our approach tries to solve problems like the use of manually established thresholds present in classical methods like identification by velocity threshold (I-VT) or identification by dispersion threshold (I-DT). We propose a modification to an adaptive threshold estimation algorithm for detecting signal impulses without the need of any user input. Also, a set of features were selected to take advantage of intrinsic characteristics of clinical electrooculography tests. The models were evaluated with signals recorded to subjects affected by Spinocerebellar Ataxia type 2 (SCA2). Results obtained by the algorithm shows accuracies over 97%, recalls over 97% and precisions over 91% for the four models evaluated.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non Spontaneous Saccadic Movements Identification in Clinical Electrooculography Using Machine Learning

Becerra-García

García−Bermúdez

Joya-Caparrós

et al. 2015

Advances in Computational Intelligence

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“…The new system performs well with the voluntary blink detection rate over 96.88% with 6 false detections among 480 blinks; then the algorithm's detection sensitivity for blinks is 93% whereas the incorrectly classified blinks is 8 during 213 blinks in the Petterssons paper [28]. Furthermore, the rate for the overall hit of the new system performs was 98% with 8 classes based on linear decoding model, which could be a good effect on the expression of decoding and on-line control.…”

Section: Discussionmentioning

confidence: 99%

Volitional and Real-Time Control Cursor Based on Eye Movement Decoding Using a Linear Decoding Model

Zhang

Wang

Zhang

et al. 2016

Computational Intelligence and Neuroscience

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The aim of this study is to build a linear decoding model that reveals the relationship between the movement information and the EOG (electrooculogram) data to online control a cursor continuously with blinks and eye pursuit movements. First of all, a blink detection method is proposed to reject a voluntary single eye blink or double-blink information from EOG. Then, a linear decoding model of time series is developed to predict the position of gaze, and the model parameters are calibrated by the RLS (Recursive Least Square) algorithm; besides, the assessment of decoding accuracy is assessed through cross-validation procedure. Additionally, the subsection processing, increment control, and online calibration are presented to realize the online control. Finally, the technology is applied to the volitional and online control of a cursor to hit the multiple predefined targets. Experimental results show that the blink detection algorithm performs well with the voluntary blink detection rate over 95%. Through combining the merits of blinks and smooth pursuit movements, the movement information of eyes can be decoded in good conformity with the average Pearson correlation coefficient which is up to 0.9592, and all signal-to-noise ratios are greater than 0. The novel system allows people to successfully and economically control a cursor online with a hit rate of 98%.

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“…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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Algorithm for automatic analysis of electro-oculographic data

Cited by 45 publications

References 43 publications

Non Spontaneous Saccadic Movements Identification in Clinical Electrooculography Using Machine Learning

Non Spontaneous Saccadic Movements Identification in Clinical Electrooculography Using Machine Learning

Volitional and Real-Time Control Cursor Based on Eye Movement Decoding Using a Linear Decoding Model

Qualitative modeling of the decision-making process using electrooculography

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