Showing papers on "Artifact (error) published in 2015"

EEG artifact removal?state-of-the-art and guidelines

Abstract: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.

Automatic identification of artifacts in electrodermal activity data

Abstract: Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

An evaluation of three commercially available metal artifact reduction methods for CT imaging

Abstract: Three commercial metal artifact reduction methods were evaluated for use in computed tomography (CT) imaging in the presence of clinically realistic metal implants: Philips O-MAR, GE's monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI monochromatic imaging with metal artifact reduction software applied (MARs). Each method was evaluated according to CT number accuracy, metal size accuracy, and streak artifact severity reduction by using several phantoms, including three anthropomorphic phantoms containing metal implants (hip prosthesis, dental fillings and spinal fixation rods). All three methods showed varying degrees of success for the hip prosthesis and spinal fixation rod cases, while none were particularly beneficial for dental artifacts. Limitations of the methods were also observed. MARs underestimated the size of metal implants and introduced new artifacts in imaging planes beyond the metal implant when applied to dental artifacts, and both the O-MAR and MARs algorithms induced artifacts for spinal fixation rods in a thoracic phantom. Our findings suggest that all three artifact mitigation methods may benefit patients with metal implants, though they should be used with caution in certain scenarios.

Isolating gait-related movement artifacts in electroencephalography during human walking

Abstract: OBJECTIVE: High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. APPROACH: We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s(-1). We then tested artifact removal methods including moving average and wavelet-based techniques. MAIN RESULTS: Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. SIGNIFICANCE: Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field. Language: en

Artifacts in Magnetic Resonance Imaging

Abstract: Artifacts in magnetic resonance imaging and foreign bodies within the patient’s body may be confused with a pathology or may reduce the quality of examinations. Radiologists are frequently not informed about the medical history of patients and face postoperative/other images they are not familiar with. A gallery of such images was presented in this manuscript. A truncation artifact in the spinal cord could be misinterpreted as a syrinx. Motion artifacts caused by breathing, cardiac movement, CSF pulsation/blood flow create a ghost artifact which can be reduced by patient immobilization, or cardiac/respiratory gating. Aliasing artifacts can be eliminated by increasing the field of view. An artificially hyperintense signal on FLAIR images can result from magnetic susceptibility artifacts, CSF/vascular pulsation, motion, but can also be found in patients undergoing MRI examinations while receiving supplemental oxygen. Metallic and other foreign bodies which may be found on and in patients’ bodies are the main group of artifacts and these are the focus of this study: e.g. make-up, tattoos, hairbands, clothes, endovascular embolization, prostheses, surgical clips, intraorbital and other medical implants, etc. Knowledge of different types of artifacts and their origin, and of possible foreign bodies is necessary to eliminate them or to reduce their negative influence on MR images by adjusting acquisition parameters. It is also necessary to take them into consideration when interpreting the images. Some proposals of reducing artifacts have been mentioned. Describing in detail the procedures to avoid or limit the artifacts would go beyond the scope of this paper but technical ways to reduce them can be found in the cited literature.

FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing

Abstract: A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.

Heart monitors and processes with accelerometer motion artifact cancellation, and other electronic systems

Abstract: A heart monitor includes a single chest accelerometer (210), an analog signal conditioning and sampling section (215) responsive to said accelerometer to produce a digital signal substantially representing acceleration, and a digital processor (220) operable to filter the acceleration signal into a signal affected by body motion and to cancel the body motion signal from the acceleration signal, thereby to produce an acceleration-based cardiac-related signal Other processes and electronic systems are also disclosed

Dual energy CT: how well can pseudo-monochromatic imaging reduce metal artifacts?

Abstract: Purpose: Dual Energy CT (DECT) provides so-called monoenergetic images based on a linear combination of the original polychromatic images. At certain patient-specific energy levels, corresponding to certain patient- and slice-dependent linear combination weights, e.g., E = 160 keV corresponds to α = 1.57, a significant reduction of metal artifacts may be observed. The authors aimed at analyzing the method for its artifact reduction capabilities to identify its limitations. The results are compared with raw data-based processing. Methods: Clinical DECT uses a simplified version of monochromatic imaging by linearly combining the low and the high kV images and by assigning an energy to that linear combination. Those pseudo-monochromatic images can be used by radiologists to obtain images with reduced metal artifacts. The authors analyzed the underlying physics and carried out a series expansion of the polychromatic attenuation equations. The resulting nonlinear terms are responsible for the artifacts, but they are not linearly related between the low and the high kV scan: A linear combination of both images cannot eliminate the nonlinearities, it can only reduce their impact. Scattered radiation yields additional noncanceling nonlinearities. This method is compared to raw data-based artifact correction methods. To quantify the artifact reduction potential of pseudo-monochromatic images, they simulated the FORBILD abdomen phantom with metal implants, and they assessed patient data sets of a clinical dual source CT system (100, 140 kV Sn) containing artifacts induced by a highly concentrated contrast agent bolus and by metal. In each case, they manually selected an optimal α and compared it to a raw data-based material decomposition in case of simulation, to raw data-based material decomposition of inconsistent rays in case of the patient data set containing contrast agent, and to the frequency split normalized metal artifact reduction in case of the metal implant. For each case, the contrast-to-noise ratio (CNR) was assessed. Results: In the simulation, the pseudo-monochromatic images yielded acceptable artifact reduction results. However, the CNR in the artifact-reduced images was more than 60% lower than in the original polychromatic images. In contrast, the raw data-based material decomposition did not significantly reduce the CNR in the virtual monochromatic images. Regarding the patient data with beam hardening artifacts and with metal artifacts from small implants the pseudo-monochromatic method was able to reduce the artifacts, again with the downside of a significant CNR reduction. More intense metal artifacts, e.g., as those caused by an artificial hip joint, could not be suppressed. Conclusions: Pseudo-monochromatic imaging is able to reduce beam hardening, scatter, and metal artifacts in some cases but it cannot remove them. In all cases, the CNR is significantly reduced, thereby rendering the method questionable, unless special post processing algorithms are implemented to restore the high CNR from the original images (e.g., by using a frequency split technique). Raw data-based dual energy decomposition methods should be preferred, in particular, because the CNR penalty is almost negligible.

Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking

Abstract: There has been a recent surge in the use of electroencephalography (EEG) as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA) and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals) with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

Distributed Signal Processing for Wireless EEG Sensor Networks

Abstract: 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.

Efficient Learning of Image Super-Resolution and Compression Artifact Removal with Semi-Local Gaussian Processes

Abstract: Improving the quality of degraded images is a key problem in image processing, but the breadth of the problem leads to domain-specific approaches for tasks such as super-resolution and compression artifact removal. Recent approaches have shown that a general approach is possible by learning application-specific models from examples; however, learning models sophisticated enough to generate high-quality images is computationally expensive, and so specific per-application or per-dataset models are impractical. To solve this problem, we present an efficient semi-local approximation scheme to large-scale Gaussian processes. This allows efficient learning of task-specific image enhancements from example images without reducing quality. As such, our algorithm can be easily customized to specific applications and datasets, and we show the efficiency and effectiveness of our approach across five domains: single-image super-resolution for scene, human face, and text images, and artifact removal in JPEG- and JPEG 2000-encoded images.

Monoenergetic extrapolation of cardiac dual energy CT for artifact reduction

Abstract: BackgroundMonoenergetic extrapolation of cardiac dual-energy computed tomography (DECT) could be useful in artifact reduction in clinical practice.PurposeTo evaluate the potential of monoenergetic extrapolation of cardiac DECT data for reducing artifacts from metal and high iodine contrast concentration.Material and MethodsWith IRB approval and in HIPAA compliance, 35 patients (22 men, 61 ± 12 years) underwent cardiac DECT with dual-source CT (100 kVp and 140 kVp). Contrast material injection protocols were adapted to the patient’s weight using non-ionic low-osmolar 370 mgI/mL iopromide. Datasets were transferred to a stand-alone workstation and dedicated monoenergetic analysis software was used for postprocessing. Reconstructions with the following five photon energies were generated: 40 keV, 60 keV, 80 keV, 100 keV, and 120 keV. Artifact severity was graded on a 5-point Likert scale (0, massive artifact; 5, absence of artifact). The size of artifact and image noise (expressed as HU) in anatomic structur...

Motion artifacts in capacitive ECG measurements: reducing the combined effect of DC voltages and capacitance changes using an injection signal.

Abstract: Capacitive electrodes are a promising alternative to the conventional adhesive electrodes for ECG measurements. They provide more comfort to the patient when integrated in everyday objects (e.g., beds or seats) for long-term monitoring. However, the application of capacitive sensors is limited by their high sensitivity to motion artifacts. For example, motion at the body-electrode interface causes variations of the coupling capacitance which, in the presence of a dc voltage across the coupling capacitor, create strong artifacts in the measurements. The origin, relevance, and reduction of this specific and important type of artifacts are studied here. An injection signal is exploited to track the variations of the coupling capacitance in real time. This information is then used by an identification scheme to estimate the artifacts and subtract them from the measurements. The method was evaluated in simulations, lab environments, and in a real-life recording on an adult's chest. For the type of artifact under study, a strong artifact reduction ranging from 40 dB for simulated data to 9 dB for a given real-life recording was achieved. The proposed method is automated, does not require any knowledge about the measurement system parameters, and provides an online estimate for the dc voltage across the coupling capacitor.

Analysis of metal artifact reduction tools for dental hardware in CT scans of the oral cavity: kVp, iterative reconstruction, dual-energy CT, metal artifact reduction software: does it make a difference?

Abstract: Metal artifacts may negatively affect radiologic assessment in the oral cavity. The aim of this study was to evaluate different metal artifact reduction techniques for metal artifacts induced by dental hardware in CT scans of the oral cavity. Clinical image quality was assessed using a Thiel-embalmed cadaver. A Catphan phantom and a polymethylmethacrylate (PMMA) phantom were used to evaluate physical-technical image quality parameters such as artifact area, artifact index (AI), and contrast detail (IQFinv). Metal cylinders were inserted in each phantom to create metal artifacts. CT images of both phantoms and the Thiel-embalmed cadaver were acquired on a multislice CT scanner using 80, 100, 120, and 140 kVp; model-based iterative reconstruction (Veo); and synthesized monochromatic keV images with and without metal artifact reduction software (MARs). Four radiologists assessed the clinical image quality, using an image criteria score (ICS). Significant influence of increasing kVp and the use of Veo was found on clinical image quality (p = 0.007 and p = 0.014, respectively). Application of MARs resulted in a smaller artifact area (p < 0.05). However, MARs reconstructed images resulted in lower ICS. Of all investigated techniques, Veo shows to be most promising, with a significant improvement of both the clinical and physical-technical image quality without adversely affecting contrast detail. MARs reconstruction in CT images of the oral cavity to reduce dental hardware metallic artifacts is not sufficient and may even adversely influence the image quality.

Closed-Loop Control of Myoelectric Prostheses With Electrotactile Feedback: Influence of Stimulation Artifact and Blanking

Abstract: Electrocutaneous stimulation is a promising approach to provide sensory feedback to amputees, and thus close the loop in upper limb prosthetic systems. However, the stimulation introduces artifacts in the recorded electromyographic (EMG) signals, which may be detrimental for the control of myoelectric prostheses. In this study, artifact blanking with three data segmentation approaches was investigated as a simple method to restore the performance of pattern recognition in prosthesis control (eight motions) when EMG signals are corrupted by stimulation artifacts. The methods were tested over a range of stimulation conditions and using four feature sets, comprising both time and frequency domain features. The results demonstrated that when stimulation artifacts were present, the classification performance improved with blanking in all tested conditions. In some cases, the classification performance with blanking was at the level of the benchmark (artifact-free data). The greatest pulse duration and frequency that allowed a full performance recovery were 400 $\mu{\rm s}$ and 150 Hz, respectively. These results show that artifact blanking can be used as a practical solution to eliminate the negative influence of the stimulation artifact on EMG pattern classification in a broad range of conditions, thus allowing to close the loop in myoelectric prostheses using electrotactile feedback.

Estimation of heart rate from photoplethysmography during physical exercise using Wiener filtering and the phase vocoder

Abstract: A system for estimation of the heart rate (HR) from the photoplethysmographic (PPG) signal during intensive physical exercises is presented. The Wiener filter is used to attenuate the noise introduced by the motion artifacts in the PPG signals. The frequency with the highest magnitude estimated using Fourier transformation is selected from the resultant de-noised signal. The phase vocoder technique is exploited to refine the frequency estimate, from which the HR in beats per minute (BPM) is finally calculated. On a publically available database of twenty three PPG recordings, the proposed technique obtains an error of 2.28 BPM. A relative error rate reduction of 18% is obtained when comparing with the state-of-the art PPG-based HR estimation methods. The proposed system is shown to be robust to strong motion artifact, produces high accuracy results and has very few free parameters, in contrast to other available approaches. The algorithm has low computational cost and can be used for fitness tracking and health monitoring in wearable devices.

Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Abstract: Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.

Visibility and artifacts of gold fiducial markers used for image guided radiation therapy of pancreatic cancer on MRI.

Abstract: Purpose: In radiation therapy of pancreatic cancer, tumor alignment prior to each treatment fraction is improved when intratumoral gold fiducial markers (from here onwards: markers), which are visible on computed tomography (CT) and cone beam CT, are used. Visibility of these markers on magnetic resonance imaging (MRI) might improve image registration between CT and magnetic resonance (MR) images for tumor delineation purposes. However, concomitant image artifacts induced by markers are undesirable. The extent of visibility and artifact size depend on MRI-sequence parameters. The authors’ goal was to determine for various markers their potential to be visible and to generate artifacts, using measures that are independent of the MRI-sequence parameters. Methods: The authors selected ten different markers suitable for endoscopic placement in the pancreas and placed them into a phantom. The markers varied in diameter (0.28–0.6 mm), shape, and iron content (0%–0.5%). For each marker, the authors calculated T 2 ∗-maps and ΔB 0-maps using MRI measurements. A decrease in relaxation time T 2 ∗ can cause signal voids, associated with visibility, while a change in the magnetic field B 0 can cause signal shifts, which are associated with artifacts. These shifts inhibit accurate tumor delineation. As a measure for potential visibility, the authors used the volume of low T 2 ∗, i.e., the volume for which T 2 ∗ differed from the background by >15 ms. As a measure for potential artifacts, the authors used the volume for which |ΔB 0| > 9.4 × 10−8 T (4 Hz). To test whether there is a correlation between visibility and artifact size, the authors calculated the Spearman’s correlation coefficient (Rs ) between the volume of low T 2 ∗ and the volume of high |ΔB 0|. The authors compared the maps with images obtained using a clinical MR-sequence. Finally, for the best visible marker as well as the marker that showed the smallest artifact, the authors compared the phantom data with in vivo MR-images in four pancreatic cancer patients. Results: The authors found a strong correlation (Rs = 1.00, p < 0.01) between the volume of low T 2 ∗ and the volume with high |ΔB 0|. Visibility in clinical MR-images increased with lower T 2 ∗. Signal shift artifacts became worse for markers with high |ΔB 0|. The marker that was best visible in the phantom, a folded marker with 0.5% iron content, was also visible in vivo, but showed artifacts on diffusion weighted images. The marker with the smallest artifact in the phantom, a small, stretched, ironless marker, was indiscernible on in vivo MR-images. Conclusions: Changes in T 2 ∗ and ΔB 0 are sequence-independent measures for potential visibility and artifact size, respectively. Improved visibility of markers correlates strongly to signal shift artifacts; therefore, marker choice will depend on the clinical purpose. When visibility of the markers is most important, markers that contain iron are optimal, preferably in a folded configuration. For artifact sensitive imaging, small ironless markers are best, preferably in a stretched configuration.

Sampling artifact in volume weighted velocity measurement. I. Theoretical modeling

Abstract: Cosmology based on large scale peculiar velocity prefers volume weighted velocity statistics. However, measuring the volume weighted velocity statistics from inhomogeneously distributed galaxies (simulation particles/halos) suffers from an inevitable and significant sampling artifact. We study this sampling artifact in the velocity power spectrum measured by the nearest particle velocity assignment method by Zheng et al., [Phys. Rev. D 88, 103510 (2013).]. We derive the analytical expression of leading and higher order terms. We find that the sampling artifact suppresses the z = 0 E-mode velocity power spectrum by similar to 10% at k = 0.1 h/Mpc, for samples with number density 10(-3) (Mpc/h)(-3). This suppression becomes larger for larger k and for sparser samples. We argue that this source of systematic errors in peculiar velocity cosmology, albeit severe, can be self-calibrated in the framework of our theoretical modelling. We also work out the sampling artifact in the density-velocity cross power spectrum measurement. A more robust evaluation of related statistics through simulations will be presented in a companion paper by Zheng et al., [Sampling artifact in volume weighted velocity measurement. II. Detection in simulations and comparison with theoretical modelling, arXiv: 1409.6809.]. We also argue that similar sampling artifact exists in other velocity assignment methods and hence must be carefully corrected to avoid systematic bias in peculiar velocity cosmology.

Investigating the possible effect of electrode support structure on motion artifact in wearable bioelectric signal monitoring

Abstract: With advances in technology and increasing demand, wearable biosignal monitoring is developing and new applications are emerging. One of the main challenges facing the widespread use of wearable monitoring systems is the motion artifact. The sources of the motion artifact lie in the skin–electrode interface. Reducing the motion and deformation at this interface should have positive effects on signal quality. In this study, we aim to investigate whether the structure supporting the electrode can be designed to reduce the motion artifact with the hypothesis that this can be achieved by stabilizing the skin deformations around the electrode. We compare four textile electrodes with different support structure designs: a soft padding larger than the electrode area, a soft padding larger than the electrode area with a novel skin deformation restricting design, a soft padding the same size as the electrode area, and a rigid support the same size as the electrode. With five subjects and two electrode locations placed over different kinds of tissue at various mounting forces, we simultaneously measured the motion artifact, a motion affected ECG, and the real-time skin–electrode impedance during the application of controlled motion to the electrodes. The design of the electrode support structure has an effect on the generated motion artifact; good design with a skin stabilizing structure makes the electrodes physically more motion artifact resilient, directly affecting signal quality. Increasing the applied mounting force shows a positive effect up to 1,000 gr applied force. The properties of tissue under the electrode are an important factor in the generation of the motion artifact and the functioning of the electrodes. The relationship of motion artifact amplitude to the electrode movement magnitude is seen to be linear for smaller movements. For larger movements, the increase of motion generated a disproportionally larger artifact. The motion artifact and the induced impedance change were caused by the electrode motion and contained the same frequency components as the applied electrode motion pattern. We found that stabilizing the skin around the electrode using an electrode structure that manages to successfully distribute the force and movement to an area beyond the borders of the electrical contact area reduces the motion artifact when compared to structures that are the same size as the electrode area.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

Abstract: Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.