Removal of Eye Blink Artifacts From EEG Signals Based on Cross-Correlation

Li, Juefeng; Yang, Xiaohu; Ding, Yongwei; Zhu, Xueping

doi:10.1109/iccit.2007.187

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Most of the current approaches to Ocular Artefact removal are "blind" and include removal of blink regions (Yoo, Basa and Lee, 2007), wavelet decomposition (Kumar, Arumuganathan, Sivakumar, et al, 2008), Independent Components Analysis (Vigário, 1997) or use Electrooculogram recordings (EOG) to then subtract this from the EEG (Jervis, Coelho and Morgan, 1989). "Blind" approaches have the downfall of the artefact removal being performed generically to the whole signal, so there is a step in identifying what is and what is not an artefact, which is prone to error.…”

Section: Introductionmentioning

confidence: 99%

EEG and Eye-Tracking Integration for Ocular Artefact Correction

Lourenco

Abbott

Faisal

2014

Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics

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Electroencephalograms (EEG) are a widely used brain signal recording technique. The information conveyed in these recordings can be an extremely useful tool in the diagnosis of some diseases and disturbances, as well as in the development of non-invasive Brain-Machine Interfaces (BMI). However, the non-invasive electrical recording setup comes with two major downsides, a. poor signal-to-noise ratio and b. the vulnerability to any external and internal noise sources. One of the main sources of artefacts are eye movements due to the electric dipole between the cornea and the retina. We have previously proposed that monitoring eye-movements provide a complementary signal for BMIs. He we propose a novel technique to remove eye-related artefacts from the EEG recordings. We couple Eye Tracking with EEG allowing us to independently measure when ocular artefact events occur and thus clean them up in a targeted manner instead of using a "blind" artefact clean up correction technique. Three standard methods of artefact correction were applied in an event-driven, supervised manner: 1. Independent Components Analysis (ICA), 2. Wiener Filter and 3. Wavelet Decomposition and compared to "blind" unsupervised ICA clean up. These are standard artefact correction approaches implemented in many toolboxes and experimental EEG systems and could easily be applied by their users in an event-driven manner. Already the qualitative inspection of the clean up traces show that the simple targeted artefact event-driven clean up outperforms the traditional "blind" clean up approaches. We conclude that this justifies the small extra effort of performing simultaneous eye tracking with any EEG recording to enable simple, but targeted, automatic artefact removal that preserves more of the original signal.

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Section: Introductionmentioning

confidence: 99%

EEG and Eye-Tracking Integration for Ocular Artefact Correction

Lourenco

Abbott

Faisal

2014

Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics

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Spectral Subtraction Based Emotion Recognition Using EEG

Min

Kwon

Hong

2011

Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments

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In this paper, an EEG-based emotion database was reconstructed using spectral subtraction, and recognition performances were evaluated. For subtraction, we created two types of databases. One database included facial expression readings, and the other included both emotion and facial expression readings. A reconstructed database containing pure emotional information was achieved by spectral subtraction, and compared with the original recorded data of emotion and facial expression readings. Facial expression illustrations and the International Affective Picture System were used for inducing facial expressions and feelings. EEG data was recorded after an emotion was excited or while imitating a particular facial expression. By subtracting the database of information related to facial expressions from a database about facial expressions and emotions, pure information about emotion was created. The method used to separate emotion and expression in a database was spectral subtraction. Recognition experiments were classified into six types of emotions. Using the original database, the true emotion could be guessed from EEG readings 29.9% of the time, but using the reconstructed database resulted in an 81.7% recognition rate.

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Detection of eye blinks from EEG signals for home lighting system activation

Rani

Mansor

2009

2009 6th International Symposium on Mechatronics and Its Applications

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Removal of Eye Blink Artifacts From EEG Signals Based on Cross-Correlation

Cited by 5 publications

References 23 publications

EEG and Eye-Tracking Integration for Ocular Artefact Correction

EEG and Eye-Tracking Integration for Ocular Artefact Correction

Spectral Subtraction Based Emotion Recognition Using EEG

Detection of eye blinks from EEG signals for home lighting system activation

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