Abstract: Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a Gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than −16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.

Abstract: Eye movements introduce large artifacts to electroencephalographic recordings (EEG) and thus render data analysis difficult or even impossible. Trials contaminated by eye movement and blink artifacts have to be discarded, hence in standard EEG-paradigms subjects are required to fixate on the screen. To overcome this restriction, several correction methods including regression and blind source separation have been proposed. Yet, there is no automated standard procedure established. By simultaneously recording eye movements and 64-channel-EEG during a guided eye movement paradigm, we investigate and review the properties of eye movement artifacts, including corneo-retinal dipole changes, saccadic spike potentials and eyelid artifacts, and study their interrelations during different types of eye- and eyelid movements. In concordance with earlier studies our results confirm that these artifacts arise from different independent sources and that depending on electrode site, gaze direction, and choice of reference these sources contribute differently to the measured signal. We assess the respective implications for artifact correction methods and therefore compare the performance of two prominent approaches, namely linear regression and independent component analysis (ICA). We show and discuss that due to the independence of eye artifact sources, regression-based correction methods inevitably over- or under-correct individual artifact components, while ICA is in principle suited to address such mixtures of different types of artifacts. Finally, we propose an algorithm, which uses eye tracker information to objectively identify eye-artifact related ICA-components (ICs) in an automated manner. In the data presented here, the algorithm performed very similar to human experts when those were given both, the topographies of the ICs and their respective activations in a large amount of trials. Moreover it performed more reliable and almost twice as effective than human experts when those had to base their decision on IC topographies only. Furthermore, a receiver operating characteristic (ROC) analysis demonstrated an optimal balance of false positive and false negative at an area under curve (AUC) of more than 0.99. Removing the automatically detected ICs from the data resulted in removal or substantial suppression of ocular artifacts including microsaccadic spike potentials, while the relevant neural signal remained unaffected. In conclusion the present work aims at a better understanding of individual eye movement artifacts, their interrelations and the respective implications for eye artifact correction. Additionally, the proposed ICA-procedure provides a tool for optimized detection and correction of eye movement-related artifact components.

Abstract: The performance of pulse oximeters is highly influenced by motion artifacts (MAs) in photoplethysmographic (PPG) signals. In this paper, we propose a simple and efficient approach based on adaptive step-size least mean squares (AS-LMS) adaptive filter for reducing MA in corrupted PPG signals. The presented method is an extension to our prior work on efficient use of adaptive filters for reduction of MA in PPG signals. The novelty of the method lies in the fact that a synthetic noise reference signal for an adaptive filtering process, representing MA noise, is generated internally from the MA-corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Thus, the generated noise reference signal is then applied to the AS-LMS adaptive filter for artifact removal. While experimental results proved the efficacy of the proposed scheme, the merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal. In addition to arterial oxygen saturation estimation, the artifact reduction method facilitated the waveform contour analysis on artifact-reduced PPG, and the conventional parameters were evaluated for assessing the arterial stiffness.

Abstract: Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

Abstract: The x-ray exposure to patients has become a major concern in computed tomography (CT) and minimizing the radiation exposure has been one of the major efforts in the CT field. Due to plenty high-attenuation tissues in the human chest, under low-dose scan protocols, thoracic low-dose CT (LDCT) images tend to be severely degraded by excessive mottled noise and non-stationary streak artifacts. Their removal is rather a challenging task because the streak artifacts with directional prominence are often hard to discriminate from the attenuation information of normal tissues. This paper describes a two-step processing scheme called 'artifact suppressed large-scale nonlocal means' for suppressing both noise and artifacts in thoracic LDCT images. Specific scale and direction properties were exploited to discriminate the noise and artifacts from image structures. Parallel implementation has been introduced to speed up the whole processing by more than 100 times. Phantom and patient CT images were both acquired for evaluation purpose. Comparative qualitative and quantitative analyses were both performed that allows conclusion on the efficacy of our method in improving thoracic LDCT data.

Abstract: Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a “ground truth” signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this “ground truth,” together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform.

Abstract: Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis (ICA) for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-continuous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine (SVM) is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.

Abstract: A noninvasive method for imaging the human brain during mobile activities could have far reaching benefits for studies of human motor control, for research and treatment of neurological disabilities, and for brain-controlled powered prosthetic limbs or orthoses. Several recent studies have demonstrated that electroencephalography (EEG) can be used to image the brain during locomotion provided that signal processing techniques, such as independent Component Analysis (ICA), are used to parse electrocortical activity from artifact contaminated EEG. However, these studies used high-density 256-channel EEG sensor arrays, which are likely too time-consuming to setup in a clinical or field setting. Therefore, it is important to evaluate how reducing the number of EEG channel signals affects the electrocortical source signals that can be parsed from EEG recorded during standing and walking while concurrently performing a visual oddball discrimination task. Specifically, we computed temporal and spatial correlations between electrocortical sources parsed from high-density EEG and electrocortical sources parsed from reduced-channel subsets of the original high-density EEG. For this task, our results indicate that on average an EEG montage with as few as 35 channels may be sufficient to record the two most dominate electrocortical sources (temporal and spatial R2 > 0.9). Correlations for additional electrocortical sources decreased linearly such that the least dominant sources extracted from the 35 channel dataset had temporal and spatial correlations of approximately 0.7. This suggests that for certain applications the number of EEG sensors used for mobile brain imaging could be vastly reduced, but researchers and clinicians must consider the expected distribution of relevant electrocortical sources when determining the number of EEG sensors necessary for a particular application.

Abstract: A motion artifact removal method with a two-stage cascade LMS adaptive filter is proposed for an ambulatory ECG monitoring system. The first LMS stage consisting of analog feedback prevents the signal saturation to reduce the input dynamic range. An adaptive step-size LMS algorithm is introduced and employed for the second LMS stage. The adaptive step-size algorithm can achieve fast convergence to track large sudden motion artifact quickly, while preventing the distortion of the ECG component. The filtering performance is evaluated by the heart beat detection, measured by sensitivity (Se) and positive predictive value (+p), and the performance is increased by 9.8% and 6.48%, respectively, compared to the unfiltered signal at the worst case with -25dB SNR. The proposed motion artifact method is implemented on an ambulatory ECG monitoring module, and the real-time measurement shows a significant performance improvement.

Abstract: Block based transform coding is one of the most popular techniques for image and video compression. However it suffers from several visual quality degradation factors, most notably from blocking artifacts. The subjective picture quality degradation caused by blocking artifacts, in general, does not agree well with the popular objective quality measure such as PSNR. A new image quality assessment method that detects and measures strength of blocking artifacts for block based transform coded images is proposed. In order to characterize the blocking artifacts, we utilize two observations: if blocking artifacts occur on the block boundary, the pixel value changes abruptly across the boundary and the same pixel values usually span along the entire length of the boundary. The proposed method operates only on a single block boundary to detect blocking artifacts. When a boundary is classified as having blocking artifacts, corresponding blocking artifact strength is also computed. Average values of those blocking artifact strengths are converted into a single number representing the subjective image quality. Experiments on various JPEG compressed images with various bit rates demonstrated that the proposed blocking artifacts measuring value matches well with the subjective image quality judged by human observers.

Abstract: The presence of motion artifacts in the photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in real time and continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection, which is based on the analysis of the variations in the time and period domain characteristics of the PPG signal. The extracted features are ranked using a feature selection algorithm (NMIFS) and the best features are used in a Support Vector Machine classification model to distinguish between clean and corrupted sections of the PPG signal. The results achieved by the current algorithm (SE: 0.827 and SP: 0.927) show that both time and especially period domain features play an important role in the discrimination of motion artifacts from clean PPG pulses.

Abstract: Prior research has found that toddlers will form enduring artifact categories after direct exposure to an adult using a novel tool. Four studies explored whether 2- (N = 48) and 3-year-olds (N = 32) demonstrate this same capacity when learning by eavesdropping. After surreptitiously observing an adult use 1 of 2 artifacts to operate a bell via a monitor, 3-year-olds returned to the demonstrated kind of tool as “for” the task and avoided it for an alternative task over 2 days. Two-year-olds performed similarly after eavesdropping on someone with more discriminable artifacts via the method of a window rather than a monitor. These results demonstrate that toddlers can acquire enduring artifact categories after less than 40 s of surreptitious observation.

Abstract: Electrooculography (EOG) signal is one of the useful electro-physiological signals. The EOG signals provide information about eye movements that can be used as a control signal in human-computer interface (HCI). Usually, eight-directional movements, including up, down, right, left, up-right, up-left, down-right and down-left, are proposed. Development of the EOG signal classification has been shown more increasing interest in the last decade; however, the effect of noises on classification system is a major problem to degrade the usefulness of EOG-based HCI. A robust classification algorithm of the eight movements is proposed, in which this technique can conduct the effect of noises in EOG signal, particularly for involuntary movements and eye-blink artifacts. The proposed algorithm was based on the onset analysis, feature extraction, the first derivative technique and threshold classification. Eight beneficial time domain features were proposed including the peak and the valley amplitude positions, and the upper and the lower wavelengths of two EOG channels, vertical and horizontal channels. Based on the optimal threshold values and conditions, the results showed that classification accuracy reached 100% for three-subject testing. In addition, the first derivative technique was additionally implemented in order to avoid the eye-blink artifact and other eight time domain features, that is, peak amplitude and area under curve, have been investigated for use in advanced HCI interfaces, notably, eye activity and eye writing recognitions. Key words: Electrooculography signal, eye motions, eye blink artifacts, feature extraction, interference, noises, non-pattern recognition, robustness, threshold analysis.

Abstract: BACKGROUND AND PURPOSE: MR imaging of moving patients can be challenging and motion correction techniques have been proposed though some have associated new artifacts. The objective of this study was to semiquantitatively compare brain MR images of moving patients obtained at 1.5T by using partially radial and rectilinear acquisition techniques. MATERIALS AND METHODS: FLAIR, T2-, T1-, and contrast-enhanced T1-weighted image sets of 25 patients (14–94 years) obtained by using BLADE (like PROPELLER, a partially radial acquisition) and rectilinear techniques in the same imaging session were compared by 2 neuroradiologists in terms of extent of the motion artifact, image quality, and lesion visibility. ICC between opinions of the evaluators was calculated. RESULTS: Of the total of 70 image sets, the motion artifact was small in the partially radial images in 43 and in the rectilinear images in 13, and the opinions of the evaluators were discordant in the remaining 14 sets (ICC = 0.63, P CONCLUSIONS: The data demonstrate that our application of BLADE sequences reduces the extent of motion artifacts in brain images of moving patients, improving image quality and lesion characterization.

Abstract: Motion artifact noise in ECG processing is difficult to remove since its spectrum is known to overlap the ECG signal spectrum The combination of wavelet based denoising and high-pass/low-pass filtering is presented and shown to provide good motion artifact noise removal capabilities The relative performance of the new technique is demonstrated using ECGs from the MIT-BIH ECG database

Abstract: To ensure the preservation of cultural heritage, artifacts such as paintings must be analyzed to diagnose physical frailties that could result in permanent damage. Advancements in digital imaging techniques and computer-aided analysis have greatly aided in such diagnoses but can limit the ability to work directly with the artifact in the field. This paper presents the implementation and application of ARtifact, a tablet-based augmented reality system that enables on-site visual analysis of the artifact in question. Utilizing real-time tracking of the artifact under observation, a user interacting with the tablet can study various layers of data registered with the physical object in situ. Theses layers, representing data acquired through various imaging modalities such as infrared thermography and ultraviolet fluorescence, provide the user with an augmented view of the artifact to aid in on-site diagnosis and restoration. Intuitive interaction techniques further enable targeted analysis of artifact-related data. We present a case study utilizing our tablet system to analyze a 16th century Italian hall and highlight the benefits of our approach.

Abstract: Post-stroke rehabilitation is one of the major health-care challenges. Robotic-aided therapy, if coupled with adequate monitoring techniques, is able to provide task-specific highly-intensive repetitive treatments that may facilitate patients' motor recovery. The EEG is the best non-invasive brain imaging modality in terms of sensors lightness, noninvasiveness, and temporal resolution, however artifact contamination has always made it difficult for scientists to use it in combination with lower limb robotic-aided rehabilitation. In this work we present for the first time REMOV, a method that combines various routines for the removal of EEG artifacts during Hocoma-Lokomat lower-limb rehabilitation. REMOV includes various preprocessing, abnormal data removal, channels rejection, ocular artifacts rejection and fine-tuning steps. This study, although at its preliminar state, may help scientists to use the EEG as brain imaging technique during Lokomat rehabilitation, and will hopefully pave the way to further advancements on EEG artifacts removal.

Abstract: EEG is the most popular potential non-invasive interface, mainly due to its fine temporal resolution, ease of use, portability and low set-up cost. However, it has some limitations. The main limitation is that EEG is frequently contaminated by various artifacts. In this paper, a novel approach to classify various electromyography and electrooculography artifacts in EEG signals is presented. EEG signals were acquired at the Department of Electrical and Electronics Engineering Karadeniz Technical University from three healthy human subjects in age groups between 28 and 30 years old and on two different days. Extracted feature vectors based on root mean square, polynomial fitting and Hjorth descriptors were classified by k-nearest neighbor algorithm. The proposed method was successfully applied to the data sets and achieved an average classification rate of 94% on the test data. Streszczenie. W artykule przedstawiono nową metode analizy sygnalow w technice EEG pod wzgledem klasyfikacji bledow zakloceniowych w wynikach badan elektromiografii i elektrookulografii. Badanie przeprowadzone zostalo na podstawie rzeczywistych wynikow EEG. (Klasyfikacja zaklocen sygnalow w technice EEG w badaniach EMG i EOG)

Abstract: A proximity threshold of an avatar is defined with respect to proximity to an artifact located within a virtual universe domain Activity by the avatar within the virtual universe domain is tracked, with activity data generated from the tracking The activity data is analyzed to determine proximity of the avatar to the artifact within the proximity threshold, and a report is generated from the analyzing, the report noting a determined proximity of the avatar to the artifact within the proximity threshold In one aspect, the report is provided to a supervisory entity

Abstract: The portability, low cost, and non-ionizing radiation associated with medical ultrasound suggest that it has potential as a superior alternative to X-ray for bone imaging. However, when conventional ultrasound imaging systems are used for bone imaging, clinical acceptance is frequently limited by artifacts derived from reflections occurring away from the main axis of the acoustic beam. In this paper, the physical source of off-axis artifacts and the effect of transducer geometry on these artifacts are investigated in simulation and experimental studies. In agreement with diffraction theory, the sampled linear-array geometry possessed increased off-axis energy compared with single-element piston geometry, and therefore, exhibited greater levels of artifact signal. Simulation and experimental results demonstrated that the lineararray geometry exhibited increased artifact signal when the center frequency increased, when energy off-axis to the main acoustic beam (i.e., grating lobes) was perpendicularly incident upon off-axis surfaces, and when off-axis surfaces were specular rather than diffusive. The simulation model used to simulate specular reflections was validated experimentally and a correlation coefficient of 0.97 between experimental and simulated peak reflection contrast was observed. In ex vivo experiments, the piston geometry yielded 4 and 6.2 dB average contrast improvement compared with the linear array when imaging the spinous process and interlaminar space of an animal spine, respectively. This work indicates that off-axis reflections are a major source of ultrasound image artifacts, particularly in environments comprising specular reflecting (i.e., bone or bonelike) objects. Transducer geometries with reduced sensitivity to off-axis surface reflections, such as a piston transducer geometry, yield significant reductions in image artifact.

Abstract: This paper presents a moving average method for estimating and removing electrocardiogram (ECG) artifact in surface electromyography (sEMG) recordings. This method does not require an ECG-only recording (e.g., with muscles relaxed), which is often required by other methods. The moving average method is compared to a common template subtraction method using sEMG recordings that were contaminated by adding ECG recordings. The performance of the moving average method is comparable to the template subtraction method. It provides superior performance at low signal-to-noise ratios (SNR) and is less sensitive to SNR.