Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals

Gao, Jun; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chunlei

doi:10.1007/s10548-009-0131-4

Cited by 76 publications

(45 citation statements)

References 26 publications

(33 reference statements)

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“…Equation (10) can be obtained through the EVD on the covariance matrix C x of e x, where e x denotes the random column vector of X.…”

Section: Preprocessingmentioning

confidence: 99%

“…The EEG signals are the electrical potential recordings captured from the scalp sensors, where the electrical potentials mean that the components of the brain activity [3] are mixed. In addition, the EEG signals may be contaminated by the artifacts of eye activity or muscular activity [4][5][6][7][8][9][10]. Therefore, analyzing EEG signals becomes a challenge.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, analyzing EEG signals becomes a challenge. In the brain research, the independent component analysis (ICA) algorithm is regarded as a useful method to study the brain activity through EEG signals [4][5][6][7][8][9][10]. The ICA algorithm is developed to solve the blind source separation (BSS) problem such that the mixed-up signals can be separated [11][12][13][14][15][16][17][18][19] and the brain activity information can be revealed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cost-Effective and Variable-Channel FastICA Hardware Architecture and Implementation for EEG Signal Processing

Van¹,

Huang²,

Lu³

2015

J Sign Process Syst

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This paper proposes a cost-effective and variable-channel floating-point fast independent component analysis (FastICA) hardware architecture and implementation for EEG signal processing. The Gram-Schmidt orthonormalization based whitening process is utilized to eliminate the use of the dedicated hardware for eigenvalue decomposition (EVD) in the FastICA algorithm. The proposed two processing units, PU 1 and PU 2 , in the presented FastICA hardware architecture can be reused for the centering operation of preprocessing and the updating step of the fixed-point algorithm of the FastICA algorithm, and PU 1 is reused for Gram-Schmidt orthonormalization operation of preprocessing and fixed-point algorithm to reduce the hardware cost and support 2-to-16 channel FastICA. Apart from the FastICA processing, the proposed hardware architecture supports re-reference, synchronized average, and moving average functions. The cost-effective and variable-channel FastICA hardware architecture is implemented in 90 nm 1P9M complementary metal-oxidesemiconductor (CMOS) process. As a result, the FastICA hardware implementation consumes 19.4 mW at 100 MHz with a 1.0 V supply voltage. The core size of the chip is 1.43 mm 2 . From the experimental results, the presented work achieves satisfactory performance for each function.

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“…Equation (10) can be obtained through the EVD on the covariance matrix C x of e x, where e x denotes the random column vector of X.…”

Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cost-Effective and Variable-Channel FastICA Hardware Architecture and Implementation for EEG Signal Processing

Van¹,

Huang²,

Lu³

2015

J Sign Process Syst

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show abstract

“…In order to obtain reliable results, automatic response detection and data classification for plant electrical signals were to be implemented. Many studies reported artifacts detection methods for EEG and EKG analysis [16]- [20]. These methods were appropriate for human biological signals and offline analysis.…”

Section: Ozone Critical Level Detectionmentioning

confidence: 99%

A WSN for ground-level ozone monitoring based on plant electrical activity analysis

Morosi

Dolfi

et al. 2015

2015 International Wireless Communications and Mobile Computing Conference (IWCMC)

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Abstract-This paper describes an innovative monitoring technology for detecting ground-level ozone pollution, b ased on the deployment of a network of wireless devices connected to a collection of plants, used as bio-sensors. Such devices retrieve and transmit the electrical activity signals experienced in plants, used to monitor environmental conditions. The distributed plants as sensors infrastructure communicates wirelessly with a weather station, equipped with meteorological sensors as well as data logging and both wireless and wired communication capabilities. A back end server collects and analyze real-time data from the station and delivers environmental and ozone pollution information to users through a web portal. In order to perform a classification on the level of ozone exposure, a correlation-based approach has been used. The system is implemented in pilot phase with a sensing infrastructure of four plants and started operation in November 2014. As the data collection volume increases, more accurate classification techniques can be performed.

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“…Afterwards, this classifier automatically identifies eye-artefact-independent components [9,13]. Another type of technique for effectively reducing ocular artefacts is the regression-based method [14,15]. Good reference EOG channels are used and the regression coefficients that describe the transmission from these EOG channels to EEG channels are calculated.…”

Section: Introductionmentioning

confidence: 99%

Eye Movement Artefact Suppression Using Volterra Filter for Electroencephalography Signals

et al. 2015

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Ocular artefacts are the most important form of interference in electroencephalography (EEG) signals. These eye movements have high amplitude and a frequency band that overlaps that of physiological or brain signals. It is important to remove these ocular artefacts before analyzing EEG signals to obtain accurate information about brain activity and avoid mistakes in its interpretation. This study presents an ocular artefact removal method based on a Volterra filter (VF) with a multichannel structure. Based on applications with various real and synthetic signals, the accuracy of the VF is compared with those of existing methods based on principal component analysis, support vector machines, and independent component analysis. The VF method has lower values of normalized mean square error, correlation coefficient, and spectral content change, and higher eye reduction, providing a good trade-off between removing artefacts and preserving inherent brain activities. The VF method can thus serve as an effective framework for substantially removing eye interference in EEG recordings.

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Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals

Cited by 76 publications

References 26 publications

Cost-Effective and Variable-Channel FastICA Hardware Architecture and Implementation for EEG Signal Processing

Cost-Effective and Variable-Channel FastICA Hardware Architecture and Implementation for EEG Signal Processing

A WSN for ground-level ozone monitoring based on plant electrical activity analysis

Eye Movement Artefact Suppression Using Volterra Filter for Electroencephalography Signals

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