Effective removal of eye-blink artifacts in EEG signals with semantic segmentation

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

Download Full-text

Study and Detection of Eye Blink Artifacts in EEG Signals

2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) ◽

10.1109/sceecs.2018.8546907 ◽

2018 ◽

Author(s):

Pallavi Chaturvedi ◽

Lalita Gupta

Keyword(s):

Eeg Signals ◽

Eye Blink

Download Full-text

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

2007 International Conference on Convergence Information Technology (ICCIT 2007) ◽

10.1109/iccit.2007.4420546 ◽

2007 ◽

Cited By ~ 1

Author(s):

Kil-Sang Yoo ◽

Tristan Basa ◽

Won-Hyung Lee

Keyword(s):

Cross Correlation ◽

Eeg Signals ◽

Eye Blink

Download Full-text

Detection of Human Brain Activities in a Normal Human being

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9796.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 4004-4007

Keyword(s):

Human Brain ◽

Motor Imagery ◽

Crucial Role ◽

Software Tool ◽

Eeg Signals ◽

Eye Blink ◽

Eye Blinks ◽

Eeg Data ◽

Normal Human ◽

The Brain

Detection of artifacts produced in EEG data by eye blinks is a very common problem in EEG research. In this paper we address the detection of eye blink artifacts in a motor imagery (MI) EEG data. Artifacts are nothing but some kind of disturbances present in the brain signal whose origin is not the brain itself. Detection of unwanted artifacts plays a crucial role to acquire artifact free and clean brain EEG signals to analyze and detect brain activities. There are generally two ways of generation of artifacts. From a recorded signal most common and important artifacts in the form of eye blinks are recognized and encapsulated. In this paper a new software tool named BRAINSTORM is introduced for the detection of eye blink artifacts.

Download Full-text

Removal of Eye Blink Artifacts From EEG Signals Using Sparsity

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2017.2771783 ◽

2018 ◽

Vol 22 (5) ◽

pp. 1362-1372 ◽

Cited By ~ 9

Author(s):

S. R. Sreeja ◽

Rajiv Ranjan Sahay ◽

Debasis Samanta ◽

Pabitra Mitra

Keyword(s):

Eeg Signals ◽

Eye Blink

Download Full-text

Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

Brain Sciences ◽

10.3390/brainsci9120352 ◽

2019 ◽

Vol 9 (12) ◽

pp. 352

Author(s):

Mohammad Shahbakhti ◽

Maxime Maugeon ◽

Matin Beiramvand ◽

Vaidotas Marozas

Keyword(s):

Wavelet Transform ◽

Correlation Coefficient ◽

Signal To Noise Ratio ◽

Low Complexity ◽

Stationary Wavelet Transform ◽

Eeg Signals ◽

Real Time Processing ◽

Eye Blink ◽

Eye Blinks ◽

Components Analysis

The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing.

Download Full-text