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(searched for: Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm)
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Dang-Khoa Tran, Thanh-Hai Nguyen
European Journal of Engineering and Technology Research, Volume 6, pp 6-12; doi:10.24018/ejers.2021.6.4.2438

Abstract:
In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.
Jibo He, William Choi, Xiaohui Wu, Yan Yang
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Volume 59, pp 1607-1611; doi:10.1177/1541931215591348

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Jiaxin Ma, Yu Zhang, , Fumitoshi Matsuno
IEEE Transactions on Biomedical Engineering, Volume 62, pp 876-889; doi:10.1109/tbme.2014.2369483

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Hai Thanh Nguyen, Toi Van Vo, Trung Van Nguyen
Science and Technology Development Journal, Volume 16, pp 18-28; doi:10.32508/stdj.v16i4.1608

Abstract:
This paper presents a study control of an electric wheelchair based on ElectroEncephaloGraphy (EEG). The directions of wheelchair are controlled by eye activities. A mean threshold algorithm is proposed to detect eye activities using EEG technique. The activities of eyes such as blinking two eyes, glanced left and glanced right related to the delta area of human brain are investigated. Before analyzing the EEG data, original data are filtered to reduce noise or artifacts by a band-pass filter. The proposed threshold method is applied to distinguish the phenomenon of eye activities. This study is useful for creating a BCI system such as wheelchair control. Experimental results show that the proposed threshold approach is the effectiveness.
Andrea Mognon, , Lorenzo Bruzzone,
Published: 6 January 2011
by Wiley
Psychophysiology, Volume 48, pp 229-240; doi:10.1111/j.1469-8986.2010.01061.x

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Belkoura Lotfi
Published: 1 January 2010
Frontiers in Neuroscience, Volume 4; doi:10.3389/conf.fnins.2010.06.00064

Abstract:
Single-channel EEG systems are very useful in EEG based applications where real time processing, low computational complexity and low cumbersomeness are critical constrains. These include brain-computer-interface and biofeedback devices and also some clinical applications such as EEG recording on babies or Alzheimer's disease recognition. In this paper we address the problem of eye blink artifacts detection in such systems. We study an algebraic approach based on numerical differentiation, which is recently introduced from operational calculus. The occurrence of an artifact is modeled as an irregularity which appears explicitly in the time (generalized) derivative of the EEG signal as a delay. Manipulating such delay is easy with the operational calculus and it leads to a simple joint detection and localization algorithm. While the algorithm is devised based on continuous-time arguments, the final implementation step is fully realized in a discrete-time context, using very classical discrete-time FIR filters. The proposed approach is compared with three other approaches: (1) the very basic threshold approach, (2) the approach that combines the use of median filter, matched filter and nonlinear energy operator (NEO) and (3) the wavelet based approach. Comparison is done on: (a) the artificially created signal where the eye activity is synthesized from real EEG recordings and (b) the real single-channel EEG recordings from 32 different brain locations. Results are presented with Receiver Operating Characteristics curves. The results show that the proposed approach compares to the other approaches better or as good as, while having lower computational complexity with simple real time implementation. Comparison of the results on artificially created and real signal leads to conclusions that with detection techniques based on derivative estimation we are able to detect not only eye blink artifacts, but also any spike shaped artifact, even if it is very low in amplitude.
, Mamadou Mboup, Christophe Pouzat, Lotfi Belkoura
VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 pp 175-178; doi:10.1007/978-3-642-12197-5_38

Abstract:
International audienceSingle channel EEG systems are very useful in EEG based applications where real time processing, low computational complexity and low cumbersomeness are critical constrains. These include brain-computer interface and biofeedback devices and also some clinical applications such as EEG recording on babies or Alzheimer's disease recognition. In this paper we address the problem of eye blink artifacts detection in such systems. We study an algebraic approach based on numerical differentiation, which is recently introduced from operational calculus. The occurrence of an artifact is modeled as an irregularity which appears explicitly in the time (generalized) derivative of the EEG signal as a delay. Manipulating such delay is easy with the operational calculus and it leads to a simple joint detection and localization algorithm. While the algorithm is devised based on continuous-time arguments, the final implementation step is fully realized in a discrete-time context, using very classical discrete-time FIR filters. The proposed approach is compared with three other approaches: (1) the very basic threshold approach, (2) the approach that combines the use of median filter, matched filter and nonlinear energy operator (NEO) and (3) the wavelet based approach. Comparison is done on: (a) the artificially created signal where the eye activity is synthesized from real EEG recordings and (b) the real single channel EEG recordings from 32 different brain locations. Results are presented with Receiver Operating Characteristics curves. The results show that the proposed approach compares to the other approaches better or as good as, while having lower computational complexity with simple real time implementation. Comparison of the results on artificially created and real signal leads to conclusions that with detection techniques based on derivative estimation we are able to detect not only eye blink artifacts, but also any spike shaped artifact, even if it is very low in amplitude
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