Artifact Removal Method Research Articles

Artifact identification and removal methodologies for intracranial pressure signals: a systematic scoping review.

Intracranial pressure measurement (ICP) is an essential component of deriving of multivariate data metrics foundational to improving understanding of high temporal relationships in cerebral physiology. A significant barrier to this work is artifact ridden data. As such, the objective of this review was to examine the existing literature pertinent to ICP artifact management.

Methods: A search of five databases (BIOSIS, SCOPUS, EMBASE, PubMed, and Cochrane Library) was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines with the PRISMA Extension for Scoping Review. The search question examined the methods for artifact management for ICP signals measured in human/animals. 

Results: The search yielded 5,875 unique results. There were 19 articles included in this review based on inclusion/exclusion criteria and article references. Each method presented was categorized as: (1) valid ICP pulse detection algorithms and (2) ICP artifact identification and removal methods. Machine learning-based and filter-based methods indicated the best results for artifact management; however, it was not possible to elucidate a single most robust method.

Conclusion: There is a significant lack of standardization in the metrics of effectiveness in artifact removal which makes comparison difficult across studies. Differences in artifacts observed on patient neuropathological health and recording methodologies have not been thoroughly examined and introduce additional uncertainty regarding effectiveness. 

Significance: This work provides critical insights into existing literature pertaining to ICP artifact management as it highlights holes in the literature that need to be adequately addressed in the establishment of robust artifact management methodologies.&#xD.

A Method for Electrical Stimulus Artifact Removal Exploiting Neural Refractoriness: Validation by Contrasting Cathodic and Anodic Stimulation.

To present a novel method for removing stimulus transient that exploits the absolute refractory period of electrically excitable neural tissues. Electrical stimulation often generates significant signal artifacts that can obscure important physiological signals. Removal of the artifact and understanding latent information from these signals could provide objective measures of circuit engagement, potentially driving advancements in neuromodulation research and therapies. We conducted intracranial physiology studies on five consecutive patients with Parkinson's disease who underwent deep brain stimulation (DBS) surgery as part of their routine care. Monopolar stimuli (either cathodic or anodic) were delivered in pairs through the DBS electrode across a range of inter-stimulus intervals. Recordings from adjacent unused electrode contacts used broadband sampling and precise synchronization to generate a robust template for the stimulus transient during the absolute refractory period. These templates of stimulus transient were then subtracted from recordings at different intervals to extract and analyze the residual neural potentials. After artifact removal, the residual signals exhibited absolute and relative refractory periods with timing indicative of neural activity. Cathodic and anodic DBS pulses generated distinct patterns of local tissue activation, showing phase independence from the prior stimulus. The earliest detectable neural responses occurred at short peak latencies (ranging from 0.19 to 0.38 ms post-stimulus) and were completely or partially obscured by the stimulus artifact prior to removal. Cathodic stimuli produced stronger local tissue responses than anodic stimuli, aligning with clinical observations of lower activation thresholds for cathodic stimulation. However, cathodic and anodic pulses induced artifact patterns that were equivalent but opposite. The proposed artifact removal technique enhances prior approaches by allowing direct measurement of local tissue responses without requirements for stimulus polarity reversal, template scaling, or specialized filters. This approach could be integrated into future neuromodulation systems to visualize stimulus-evoked neural potentials that would otherwise be obscured by stimulus artifacts.

Artifact removal from ECG signals using online recursive independent component analysis

The diagnosis of cardiac abnormalities and monitoring of heart health heavily rely on Electrocardiogram (ECG) signals. Unfortunately, these signals frequently encounter interference from diverse artifacts, impeding precise interpretation and analysis. To overcome this challenge, we suggest a novel method for real-time artifact removal from ECG signals through the utilization of Online Recursive Independent Component Analysis (ORICA). Our study outlines a systematic preprocessing pipeline, adaptively estimating the mixing matrix and demixing matrix of the ICA model while streaming data is processed. Additionally, we explore the selection of appropriate ICA components and the use of relevant feature extraction techniques to enhance the quality of extracted cardiac signals. This research presents a promising solution for removing artifacts from ECG signals in real-time, paving the way for improved cardiac diagnostics and monitoring systems. Comparative analyses demonstrate significant improvements in the accuracy of subsequent ECG analysis and interpretation following the application of our ORICA-based preprocessing.

Artifact Management for Cerebral Near-Infrared Spectroscopy Signals: A Systematic Scoping Review.

Artifacts induced during patient monitoring are a main limitation for near-infrared spectroscopy (NIRS) as a non-invasive method of cerebral hemodynamic monitoring. There currently does not exist a robust "gold-standard" method for artifact management for these signals. The objective of this review is to comprehensively examine the literature on existing artifact management methods for cerebral NIRS signals recorded in animals and humans. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search yielded 806 unique results. There were 19 articles from these results that were included in this review based on the inclusion/exclusion criteria. There were an additional 36 articles identified in the references of select articles that were also included. The methods outlined in these articles were grouped under two major categories: (1) motion and other disconnection artifact removal methods; (2) data quality improvement and physiological/other noise artifact filtering methods. These were sub-categorized by method type. It proved difficult to quantitatively compare the methods due to the heterogeneity of the effectiveness metrics and definitions of artifacts. The limitations evident in the existing literature justify the need for more comprehensive comparisons of artifact management. This review provides insights into the available methods for artifact management in cerebral NIRS and justification for a homogenous method to quantify the effectiveness of artifact management methods. This builds upon the work of two existing reviews that have been conducted on this topic; however, the scope is extended to all artifact types and all NIRS recording types. Future work by our lab in cerebral NIRS artifact management will lie in a layered artifact management method that will employ different techniques covered in this review (including dynamic thresholding, autoregressive-based methods, and wavelet-based methods) amongst others to remove varying artifact types.

Characterization of Cochlear Implant Artifact and Removal Based on Multi-Channel Wiener Filter in Unilateral Child Patients.

Cochlear implants (CI) allow deaf patients to improve language perception and improving their emotional valence assessment. Electroencephalographic (EEG) measures were employed so far to improve CI programming reliability and to evaluate listening effort in auditory tasks, which are particularly useful in conditions when subjective evaluations are scarcely appliable or reliable. Unfortunately, the presence of CI on the scalp introduces an electrical artifact coupled to EEG signals that masks physiological features recorded by electrodes close to the site of implant. Currently, methods for CI artifact removal have been developed for very specific EEG montages or protocols, while others require many scalp electrodes. In this study, we propose a method based on the Multi-channel Wiener filter (MWF) to overcome those shortcomings. Nine children with unilateral CI and nine age-matched normal hearing children (control) participated in the study. EEG data were acquired on a relatively low number of electrodes (n = 16) during resting condition and during an auditory task. The obtained results obtained allowed to characterize CI artifact on the affected electrode and to significantly reduce, if not remove it through MWF filtering. Moreover, the results indicate, by comparing the two sample populations, that the EEG data loss is minimal in CI users after filtering, and that data maintain EEG physiological characteristics.

Garden of forking paths in ERP research - Effects of varying pre-processing and analysis steps in an N400 experiment.

This study tackles the Garden of Forking Paths, as a challenge for replicability and reproducibility of ERP studies. Here, we applied a multiverse analysis to a sample ERP N400 dataset, donated by an independent research team. We analyzed this dataset using 14 pipelines selected to showcase the full range of methodological variability found in the N400 literature using systematic review approach. The selected pipelines were compared in depth by looking into statistical test outcomes, descriptive statistics, effect size, data quality, and statistical power. In this way we provide a worked example of how analytic flexibility can impact results in research fields with high dimensionality such as ERP, when analyzed using standard null-hypothesis significance testing. Out of the methodological decisions that were varied, high-pass filter cut-off, artifact removal method, baseline duration, reference, measurement latency and locations, and amplitude measure (peak vs. mean) were all shown to affect at least some of the study outcome measures. Low-pass filtering was the only step which did not notably influence any of these measures. This study shows that even some of the seemingly minor procedural deviations can influence the conclusions of an ERP study. We demonstrate the power of multiverse analysis in both identifying the most reliable effects in a given study, and for providing insights into consequences of methodological decisions.

Efficient architecture for ocular artifacts removal from EEG: A Novel approach based on DWT-LMM

Medical practitioners use portable electroencephalogram (EEG) headset to identify Neuro Developmental Disorders (NDD). However, the analysis of EEG signals gets affected as the recorded electrical activity always contaminated with artifacts. A portable EEG headset requires area-efficient hardware and an effective method to remove ocular artifacts. In this paper, a method is proposed, which removes ocular artifacts efficiently and consumes less hardware. This proposed method involves determining the appropriate decomposition level to obtain approximation coefficients (ACs) and detail coefficients (DCs) for minimal loss of useful information present in the EEG signal. In addition, an algorithm is also proposed to perform wavelet thresholding on wavelet coefficients using Local Maximal and Minimal (LMM) to remove artifacts from EEG signals without disturbing the information related to brain activity. The functionality of the proposed method is verified using MATLAB R2021a tool by computing average correlation coefficient & RMSE performance metrics and the proposed method is also modeled using Verilog HDL. The results show that the proposed DWT-LMM approach had superior performance in removing artifacts from EEG signals with the average correlation coefficient and RMSE values of 0.9369 and 2.2252, respectively. The functionality of this HDL netlist is verified by the VCS simulator and synthesized by Synopsys design compiler 32 nm UMC library cells. The proposed architecture layout is generated after placement & routing using Synopsys IC Compiler tool by employing 32 nm cell libraries and the results shows that the layout consumes 6490.07 μm2 of area and 792.18 μw of power with supply voltage of 0.95 V. Hence, the proposed ocular artifacts removal method and architecture can be employed in portable/wearable brain-computer interface (BCI) devices.

Methods for Processing Physiological Artifacts in Single/Few-Channel EEG Signals

Electroencephalogram (EEG) is a non-invasive measurement method of brain electrical activity. In recent years, single/few-channel EEG has been used more and more, but various types of physiological artifacts seriously affect the analysis and wide application of single/few-channel EEG. In this paper, the regression and filtering methods, decomposition methods, blind source separation methods and machine learning methods involved in the various physiological artifacts in single/few-channel EEG are reviewed. According to the characteristics of single/few-channel EEG signals, hybrid EEG artifact removal methods for different scenarios are analyzed and summarized, mainly including single-artifact/multi-artifact scenes and online/offline scenes. In addition, the methods and metrics for validating the performance of the algorithm on semi-simulated and real EEG data are also reviewed. Finally, the development trend of single/few-channel EEG application and physiological artifact processing is briefly described.

Analysis and Comparison of Artifact Removal Techniques for Epilepsy EEG Signal

Objective: Accurate epilepsy diagnosis demands precise EEG analysis, hindered by non-neuronal artifacts. This study evaluates artifact removal methods, specifically Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD), aiming to enhance signal quality. We introduce a hybrid approach, combining ICA and EMD. Methods: ICA and EMD are applied to preprocess epilepsy EEG recordings. Quantitative evaluation metrics, including Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Standard Deviation (SD), are calculated and compared for both methods. Findings: ICA outperforms EMD, showing higher SNR and PSNR, notably in BONN and CHB-MIT datasets. ICA achieves significant reductions in MSE, RMSE, and SD. The hybrid approach surpasses existing methods, supported by quantitative data. Novelty: Rigorous application of ICA and EMD to diverse datasets quantitatively establishes ICA's superiority. The hybrid approach, backed by quantitative evidence, proves effective beyond epilepsy EEG. Conclusion: This abstract provides clear, quantitative support for ICA's superiority and the hybrid approach's efficacy, offering valuable insights into artifact removal in EEG analysis. Keywords: Epilepsy, Artifact removal, EEG, ICA, DWT, EMD, Performance metrics

Comparison of electrical microstimulation artifact removal methods for high-channel-count prostheses

BackgroundNeuroprostheses are used to electrically stimulate the brain, modulate neural activity and restore sensory and motor function following injury or disease, such as blindness, paralysis, and other movement and psychiatric disorders. Recordings are often made simultaneously with stimulation, allowing the monitoring of neural signals and closed-loop control of devices. However, stimulation-evoked artifacts may obscure neural activity, particularly when stimulation and recording sites are nearby. Several methods have been developed to remove stimulation artifacts, but it remains challenging to validate and compare these methods because the ‘ground-truth’ of the neuronal signals may be contaminated by artifacts. New methodHere, we delivered stimulation to the visual cortex via a high-channel-count prosthesis while recording neuronal activity and stimulation artifacts. We quantified the waveforms and temporal properties of stimulation artifacts from the cortical visual prosthesis (CVP) and used them to build a dataset, in which we simulated the neuronal activity and the stimulation artifacts. We illustrate how to use the simulated data to evaluate the performance of six software-based artifact removal methods (Template subtraction, Linear interpolation, Polynomial fitting, Exponential fitting, SALPA and ERAASR) in a CVP application scenario. ResultsWe here focused on stimulation artifacts caused by electrical stimulation through a high-channel-count cortical prosthesis device. We find that the Polynomial fitting and Exponential fitting methods outperform the other methods in recovering spikes and multi-unit activity. Linear interpolation and Template subtraction recovered the local-field potentials. ConclusionPolynomial fitting and Exponential fitting provided a good trade-off between the quality of the recovery of spikes and multi-unit activity (MUA) and the computational complexity for a cortical prosthesis.

Cross-Correlation of Confocal Images for Excised Breast Tissues of Total Mastectomy.

The purpose of this study is to develop an image artifact removal method for radar-based microwave breast imaging and demonstrates the detectability on excised breast tissues of total mastectomy. A cross-correlation method was proposed and measurements were conducted. A hand-held radar-based breast cancer detector was utilized to measure a breast at different orientations. Images were generated by multiplying the confocal image data from two scans after cross-correlation. The optimum reconstruction permittivity values were extracted by the local maxima of the confocal image intensity as a function of reconstruction permittivity. With the proposed cross-correlation method, the contrast of the imaging result was enhanced and the clutters were removed. The proposed method was applied to 50 cases of excised breast tissues and the detection sensitivity of 72% was achieved. With the limited number of samples, the dependency of detection sensitivity on the breast size, breast density, and tumor size were examined. The detection sensitivity was strongly influenced by the breast density. The sensitivity was high for fatty breasts, whereas the sensitivity was low for heterogeneously dense breasts. In addition, it was observed that the sensitivity was high for extremely dense breast. This is the first detailed report on the excised breast tissues.

Out-of-focus artifact removal for Fresnel incoherent correlation holography by deep learning

Fresnel incoherent correlation holography (FINCH) is a promising three-dimensional (3D) imaging method due to its incoherent illumination and inline optical configuration. However, due to information crosstalk, especially out-of-focus artifacts, the axial resolution of FINCH 3D imaging is limited, and the relationship between the artifacts and the reconstructed image is complex and nonlinear, which is difficult to obtain by traditional optical measurement methods. Here, inspired by the superiority of deep learning in nonlinear mapping over traditional physical methods, we propose and demonstrate a deep learning based out-of-focus artifact removal method for FINCH reconstruction, in which a out-of-focus segmentation convolutional neural network (CNN) is designed for obtaining accurate nonlinear relationship between the artifacts and the reconstructed image by training dataset and optimizing parameters, thereby eliminating information crosstalk in the reconstructed image. Both optical experiment and performance analysis demonstrate the effectiveness and superiority of the designed CNN in realizing out-of-focus artifacts removal. Importantly, this deep learning-based out-of-focus artifacts removal method will provide a useful strategy for realizing high quality 3D imaging of FINCH.

Artifact removal by template subtraction enables recordings of the frequency following response in cochlear-implant users

Electrically evoked frequency-following responses (eFFRs) provide insight in the phase-locking ability of brainstem of cochlear-implant (CI) users. eFFRs can potentially be used to gain insight in the individual differences in the biological limitation on temporal encoding of the electrically stimulated auditory pathway, which can be inherent to the electrical stimulation itself and/or the degenerative processes associated with hearing loss. One of the major challenge of measuring eFFRs in CI users is the process of isolating the stimulation artifact from the neural response, as both the response and the artifact overlap in time and have similar frequency characteristics. Here we introduce a new artifact removal method based on template subtraction that successfully removes the stimulation artifacts from the recordings when CI users are stimulated with pulse trains from 128 to 300 pulses per second in a monopolar configuration. Our results show that, although artifact removal was successful in all CI users, the phase-locking ability of the brainstem to the different pulse rates, as assessed with the eFFR differed substantially across participants. These results show that the eFFR can be measured, free from artifacts, in CI users and that they can be used to gain insight in individual differences in temporal processing of the electrically stimulated auditory pathway.

Detection of Electrically Evoked Auditory Steady-State Responses in Cochlear-Implant Recipients With a System Identification Based Method.

Electrically evoked auditory steady-state responses (EASSRs) can potentially be used as an objective measure to realize the automatic fitting of cochlear implants (CIs). They can be recorded using electroencephalography (EEG) and objectively detected at the modulation frequency of the stimulus. The main roadblock in using EASSRs is the presence of CI stimulation artifacts in the EEG recording. In this article, we present an improvement of a recently introduced system identification (SI) based artifact removal method. We evaluate its applicability for objective CI fitting on a larger dataset. The parameter estimation problem of the SI is solved using ordinary least squares (OLS), where an additional regularization term is added to the cost function. We compare EASSR latencies as determined by the commonly used linear interpolation artifact removal method and SI, to evaluate the artifact removal and EASSR detection quality on a dataset of 16 CI recipients and four different stimulation levels. SI can fully remove stimulation artifacts and detect EASSRs, even for recordings from ipsilateral EEG channels, where all other artifact removal methods fail so far. Using OLS with regularization prevents false positive response detection. Using SI, EASSRs can reliably be detected in EEG recordings, even for ipsilateral recording channels and recordings with lower stimulation levels. As the recordings are obtained with clinically relevant settings of the CI, they reveal the potential impact of SI on the objective fitting of CIs. We argue, that SI enables therefore a big step towards automated CI fitting with EASSRs.

Cepstral Analysis-Based Artifact Detection, Recognition, and Removal for Prefrontal EEG

This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the artifact detection task and a 82.79% accuracy on the 6-category eye movement classification task. A statistical value-based artifact removal method is proposed and evaluated on a public EEG database, where an accuracy improvement of 3.46% is obtained on the 3-category emotion classification task. In order to make a confident decision of each 5s EEG segment, the algorithm requires only 0.66M multiplication operations. Compared to the state-of-the-art approaches in artifact detection and removal, the proposed method features higher detection accuracy and lower computational cost, which makes it a more suitable solution to be integrated into a real-time and artifact robust Brain-Machine Interface (BMI).

Real-time adaptive cancellation of TENS feedback artifact on sEMG for prosthesis closed-loop control.

The prosthetic hand has been aimed to restore hand functions by estimating the user's intention via bio-signal and providing sensory feedback. Surface electromyogram (sEMG) is a widely used signal, and transcutaneous electrical nerve stimulation (TENS) is a promising method for sensory feedback. However, TENS currents can transmit through the skin and interfere as noise with the sEMG signals, referred to as "Artifact," which degrades the performance of intention estimation. In this paper, we proposed an adaptive artifact removal method that can cancel artifacts separately across different frequencies and pulse widths of TENS. The modified least-mean-square adaptive filter uses the mean of previous artifacts as reference signals, and compensate using prior information of TENS system. Also temporal separation for artifact discrimination is applied to achieve high artifact removal efficiency. Four sEMG signals-two from flexor digitorum superficialis, flexor carpi ulnaris, extensor carpi ulnaris-was collected to validate signals both offline and online experiments. We validated the filtering performance with twelve participants performing two experiments: artifact cancellation under variable conditions and a real-time hand control simulation called the target reaching experiment (TRE). The result showed that the Signal-to-Noise Ratio (SNR) increased by an average of 10.3dB, and the performance of four TRE indices recovered to the levels similar to those without TENS. The proposed method can significantly improve signal quality via artifact removal in the context of sensory feedback through TENS in prosthetic systems.

Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM)

Objective. Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals. Approach. To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects. Main results. MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5–10 ms following stimulation onset), we found that the broadband gamma power (70–170 Hz) of the denoised signal was highly correlated with the ground truth signal ( R=0.98±0.02 , Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with 94%±1.47% sensitivity and 99%±1.01% specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation. Significance. MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.

Comparison of Tiling Artifact Removal Methods in Secondary Ion Mass Spectrometry Images.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterization of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, which are acquired by rastering the primary ion beam. For such large area scans, tiling artifacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artifacts are thought to be related to a combination of sample charging, local detector sensitivity issues, and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated six different computational tiling artifact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging, and simple interpolating. To ensure robustness in the study, we applied these methods to three hyperspectral ToF-SIMS data sets and one OrbiTrapSIMS data set. Our study includes a carefully designed statistical analysis and a quantitative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artifact removal method is strongly dependent on the particulars of the data set analyzed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey; however, for some data sets, this led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on the microarray data sets. We discuss and explore these findings in depth and present general recommendations given our findings to conclude the work.

Distance compensation-based dual adaptive artifact removal algorithm in microwave breast tumor imaging system

Microwave imaging techniques have been rapidly developed in the medical field due to its non-invasive and non-radiological advantages. Pre-artifact removal is a critical procedure for desirable imaging quality produced by microwave breast imaging systems. However, current methods do not sufficiently remove pre-artifact to allow accurate localization of multiple tumors from the retrieved images. This paper presents a dual adaptive artifact removal method based on distance compensation to address issues of improving imaging quality. The method compensates for the attenuation effects due to magnetic wave propagation and adaptively selects the time entropy window threshold for grouped signals. The signals are then fed into an adaptive filter with variable filter parameters to minimize pre-artifact before imaging. Comparative tests are conducted to evaluate the merits of pre-artifact removal. The proposed method effectively reduces the pre-artifact for improving the overall quality of imaging and greatly enhances the accuracy of localizing multiple tumors.

An 8-Channel Ambulatory EEG Recording IC With In-Channel Fully-Analog Real-Time Motion Artifact Extraction and Removal.

Wereport the design, implementation, and experimental characterization of an 8-channel EEG recording IC (0.13 μm CMOS, 12 mm 2 total area) with a channel architecture that conducts both the extraction and removal of motion artifacts on-chip and in-channel. The proposed dual-path feed-forward method for artifact extraction and removal is implemented in the analog domain, hence is needless of a DSP unit for artifact estimation, and its associated high-DR ADCs and DACs employed by the state of the art for artifact replica generation. Additionally, the presented architecture improves system's scalability as it enables channels' stand-alone operation, and yields the lowest reported channel power consumption among works featuring motion artifact detection/removal. Following an experimental study on electrode-skin interface electrical characteristics for dry electrodes in the absence and presence of motions, the article presents the channel architecture, its detailed signal transfer function analysis, circuit-level implementation, and experimental characterization results. Our measurement results show an amplification voltage gain of 48.3 dB, a bandwidth of 300 Hz, rail-to-rail input DC offset tolerance, and 41.5 dB artifact suppression, while consuming 55 μW per channel. The system's efficacy in EEG motion artifact suppression is validated experimentally, and system- and circuit-level features and performance metrics of the presented design are compared with the state of the art.