Detecting eye movements in EEG for controlling devices

Gupta, Soumya; Soman, Sumit; Raj, P. Govind; Prakash, Rishi; Sailaja, S.; Borgohain, Rupam

doi:10.1109/cyberneticscom.2012.6381619

Cited by 13 publications

(12 citation statements)

References 14 publications

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“…A few authors have published work related to actually using this information. Hence, [21] detect the presence of eye blinks in the EEG data using SVM in order to allow subjects to control a wheelchair. Here, the blinks are just detected, but not characterized.…”

Section: Introductionmentioning

confidence: 99%

Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms

Roy

Charbonnier

Bonnet

2014

Biomedical Signal Processing and Control

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International audienceDue to its major safety applications, including safe driving, mental fatigue estimation is a rapidly growing research topic in the engineering field. Most current mental fatigue monitoring systems analyze brain activity through electro-encephalography (EEG). Yet eye blink analysis can also be added to help characterizing fatigue states. It usually requires the use of additional devices, such as EOG electrodes, uncomfortable to wear, or more expensive eye trackers. However, in this article, a method is proposed to evaluate eye blink parameters using frontal EEG electrodes only. EEG signals, which are generally corrupted by ocular artifacts, are decomposed into sources by means of a source separation algorithm. Sources are then automatically classified into ocular or non-ocular sources using temporal, spatial and frequency features. The selected ocular source is back propagated in the signal space and used to localize blinks by means of an adaptive threshold, and then to characterize detected blinks. The method, validated on 11 different subjects, does not require any prior tuning when applied to a new subject, which makes it subject-independent. The vertical EOG signal was recorded during an experiment lasting 90 min. in which the participants’ mental fatigue increased. The blinks extracted from this signal were compared to those extracted using frontal EEG electrodes. Very good performances were obtained with a true detection rate of 89% and a false alarm rate of 3%. The correlation between the blink parameters extracted from both recording modalities was 0.81 in average

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Section: Introductionmentioning

confidence: 99%

Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms

Roy

Charbonnier

Bonnet

2014

Biomedical Signal Processing and Control

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“…There are several successful approaches reported in the literature aiming to separation of blinking signals from EEG measurements, using techniques such as Independent Component Analysis (ICA) [16,17], wavelet analysis [18,19], a combination of the two previous [20], algebraic separation [21], and Hilbert-Huang transform (HHT) [22]. Most references found in the literature refer to blinking signals separation in the context of artifact removal, although there are some studies on the use of blinking signals for control applications [23–25]. Reference [25] presents a machine learning approach to detect eye movements and blinks to control an external device, using Common Spatial Pattern (CSP) filters during feature extraction, with an accuracy of 95%.…”

Section: Introductionmentioning

confidence: 99%

“…Most references found in the literature refer to blinking signals separation in the context of artifact removal, although there are some studies on the use of blinking signals for control applications [23–25]. Reference [25] presents a machine learning approach to detect eye movements and blinks to control an external device, using Common Spatial Pattern (CSP) filters during feature extraction, with an accuracy of 95%. Reference [26] presents a comparison of Discrete Wavelet Transform (DWT) and Hilbert-Huang Transform (HHT), both used in EEG signal feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

Rosas-Cholula

Ramírez-Cortés

Alarcón-Aquino

et al. 2013

Sensors

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This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.

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“…High-pass and low-pass zero-phase filters were applied in the range of 1-30 Hz to remove power line noise, and attenuate noise caused by body movements. For all nine subjects, the F7 and F8 channels were used as the ground channels and the AF4 and AF3 channels were removed because they are near the eyes, and most signals recorded from them were related to eye blinking and movement [16]. Moreover, baseline correction was done to remove the effects that occurred prior to the presentation of each stimulus.…”

Section: A Data Pre-processingmentioning

confidence: 99%