Eye Blink Artifact Removal Research Articles

Distributed Signal Processing for Wireless EEG Sensor Networks.

Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

Development of Mouse Cursor Control System using DC and AC Elements of Electrooculogram Signals and its Applications

The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. Using electrooculogram signals, it is possible to improve the communication abilities of those patients who can move their eyes. In this paper, we introduce the mouse cursor control system for Amyotrophic Lateral Sclerosis patients using electrooculogram signals. We propose the algorithm using alternating current and direct current of electrooculogram signals corresponding to the baseline drift problem. The focus of our proposal system was laid on noise, baseline drift and eye blink artifact removal. In order to test the effectiveness of our proposal system, we tried the experiments of the Alphabet sentence in...

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

ONLINE REMOVAL OF EYE BLINK ARTIFACT FROM SCALP EEG USING CANONICAL CORRELATION ANALYSIS BASED METHOD

Eye blink artifact, the main contamination in electroencephalography (EEG), brings serious problems for the analysis of EEG data. In this paper, an online method for eye blink artifact removal is presented. Canonical correlation analysis (CCA) is used to decompose the recorded signals containing several-channel EEG and one-channel vertical electrooculography (EOG). The identification of the artifactual component is fully automatically implemented based on evaluating the similarity between the reference EOG and decomposed CCA components. This method was compared with an independent component analysis based technique on a synthetic data set and achieved comparable performance for removing eye blink artifact. Moreover, the CCA based method is less time-consuming. The proposed method was finally implemented with Labview for removing eye blink artifact in online test. The online experiment results show that the proposed method could fulfill the identification and suppression of eye blink artifact from contaminated EEG in real-time.

Removal of the Eye-Blink Artifacts From EEGs via STF-TS Modeling and Robust Minimum Variance Beamforming

A novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on a novel space-time-frequency (STF) model of EEGs and robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, namely, an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, the vector corresponding to the spatial distribution of the EB factor, is identified using the STF model of EEGs, provided by the parallel factor analysis (PARAFAC) method. In order to reduce the computational complexity present in the estimation of the STF model using the three-way PARAFAC, the time domain is subdivided into a number of segments, and a four-way array is then set to estimate the STF-time/segment (TS) model of the data using the four-way PARAFAC. The correct number of the factors of the STF model is effectively estimated by using a novel core consistency diagnostic- (CORCONDIA-) based measure. Subsequently, the STF-TS model is shown to closely approximate the classic STF model, with significantly lower computational cost. The results confirm that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements.

A Novel Semiblind Signal Extraction Approach for the Removal of Eye-Blink Artifact from EEGs

A novel blind signal extraction (BSE) scheme for the removal of eye-blink artifact from electroencephalogram (EEG) signals is proposed. In this method, in order to remove the artifact, the source extraction algorithm is provided with an estimation of the column of the mixing matrix corresponding to the point source eye-blink artifact. The eye-blink source is first extracted and then cleaned, artifact-removed EEGs are subsequently reconstructed by a deflation method. The a priori knowledge, namely, the vector, corresponding to the spatial distribution of the eye-blink factor, is identified by fitting a space-time-frequency (STF) model to the EEG measurements using the parallel factor (PARAFAC) analysis method. Hence, we call the BSE approach semiblind signal extraction (SBSE). This approach introduces the possibility of incorporating PARAFAC within the blind source extraction framework for single trial EEG processing applications and the respected formulations. Moreover, aiming at extracting the eyeblink artifact, it exploits the spatial as well as temporal prior information during the extraction procedure. Experiments on synthetic data and real EEG measurements confirm that the proposed algorithm effectively identifies and removes the eyeblink artifact from raw EEG measurements.

An automatic identification and removal method for eye-blink artifacts in event-related magnetoencephalographic measurements

While measuring event-related magnetoencephalographic (MEG) signals using visual stimuli, eye blinks were inevitable and generated large magnetic artifacts. Since trials containing eye blinks were excluded from the analyses, the signal-to-noise ratio of the event-related signals was decreased. In this study, we propose a method to identify the eye blink magnetic artifacts and remove them automatically using independent component analysis preprocessed by principal component analysis. The method evaluates the spatiotemporal similarity between independent components and both MEG and electro-oculogram data based on a newly devised cost function. Testing of the method on event-related MEG signals measured by a 306-channel whole-head system in a visual perception task led to the successful identification and removal of eye-blink artifacts in all trials containing eye blinks from all the seven subjects.

Intelligent wireless tele-FES walking assistant system for foot drop patients

Online detection and removal of eye blink (EB) artifacts from electroencephalogram (EEG) would be very useful in medical diagnosis and brain computer interface (BCI). In this work, approaches that combine unsupervised eyeblink artifact detection with empirical mode decomposition (EMD), and canonical correlation analysis (CCA), are proposed to automatically identify eyeblink artifacts and remove them in an online manner. First eyeblink artifact regions are automatically identified and an eyeblink artifact template is extracted via EMD, which incorporates an alternate interpolation technique, the Akima spline interpolation. The removal of eyeblink artifact components relies on the elimination of EEG canonical components obtained through CCA, based on cross correlation with the extracted eyeblink artifact template. The proposed algorithm is evaluated and analyzed with respect to its ability in removing eyeblink artifacts and retaining neural information of the EEG signals. Analysis proved that the proposed algorithm, FastEMD–CCA, is efficacious in eyeblink artifact removal with an average accuracy, sensitivity, specificity and error rate of 97.9%, 97.65%, 99.22% and 2.1% respectively. The algorithm is able to clean and remove eyeblink artifacts from a 14-channel EEG of length 1 s, at an average time of 63 ms. This makes it a feasible solution for applications requiring online removal of eyeblink artifacts.

Automatic removal of eye movement and blink artifacts from EEG data using blind component separation.

Signals from eye movements and blinks can be orders of magnitude larger than brain-generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the experimental designs possible and may impact the cognitive processes under investigation. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG data. BBS is a signal-processing methodology that includes independent component analysis (ICA). In contrast to previously explored ICA-based methods for artifact removal, this method is automated. Moreover, the BSS algorithm described herein can isolate correlated electroocular components with a high degree of accuracy. Although the focus is on eliminating ocular artifacts in EEG data, the approach can be extended to other sources of EEG contamination such as cardiac signals, environmental noise, and electrode drift, and adapted for use with magnetoencephalographic (MEG) data, a magnetic correlate of EEG.