Muscle Artifacts Research Articles

Frequency Domain Adaptive Learning Algorithm for Thoracic Electrical Bioimpedance Enhancement

The Thoracic Electrical Bioimpedance (TEB) helps to determine the stroke volume during cardiac arrest. While measuring cardiac signal it is contaminated with artifacts. The commonly encountered artifacts are Baseline wander (BW) and Muscle artifact (MA), these are physiological and non-stationary. As the nature of these artifacts is random, adaptive filtering is needed than conventional fixed coefficient filtering techniques. To address this, a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario. The proposed block least mean square (BLMS) algorithm is mathematically normalized with reference to data and error. This normalization leads, block normalized LMS (BNLMS) and block error normalized LMS (BENLMS) algorithms. Various adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological signals. The ability of these techniques is measured by calculating signal to noise ratio improvement (SNRI), Excess Mean Square Error (EMSE), and Misadjustment (Mad). Among the considered algorithms, the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter parts. Hence, this adaptive artifact canceller is suitable for real time applications like wearable, remove health care monitoring units.

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

In this paper, we develop a low-cost cellular internet of medical things (IoMT)-based electrocardiogram (ECG) recorder for monitoring heart conditions and used in practical cases. In order to remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that focuses on utilizing deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for noise cancellation. Due to the practical use case, the various heart physiologies and noise levels in different amplitudes and frequencies are needed to be robust against wide-range noises in actual conditions. We utilize the MIT-BIH Apnea-ECG database (APNEA-ECG) with several different heart physiologies. Next, the different noises consisting of muscle artifacts (MA), baseline wander (BW), and electrode motion (EM) from the MIT-BIH Noise Stress Test Database (NSTDB) and random noise, are added to the signals. The main focus of the noise generation part is the fast Fourier transformation (FFT) of the simulated noisy signal and the practical noisy signal has a maximum cross-correlation to gain a better morphological resemblance between realistic signals and the prepared datasets. Based on the results, DeepRTSNet outperforms prior learning-based methods and conventional non-learning approaches in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD). Moreover, outcomes reveal that DeepRTSNet has an extraordinary performance with a certain amount of further complexity than others.

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder.

This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The existing EEG denoising methods make use of decomposition, thresholding and filtering techniques. In the proposed approach, EEG signals are first transformed to orthogonal domain using Tchebichef moments before feeding to the proposed architecture. A new hyper-parameter ( α ) is introduced which refers to the fractional order with respect to which gradients are calculated during back-propagation. It is observed that by tuning α , the quality of the restored signal improves significantly. Motivated by the high usage of portable low energy devices which make use of compressed deep learning architectures, the trainable parameters of the proposed architecture are compressed using randomized singular value decomposition (RSVD) algorithm. The experiments are performed on the standard EEG datasets, namely, Mendeley and Bonn. The study shows that the proposed fractional and compressed architecture performs better than existing state-of-the-art signal denoising methods.

Non-invasive, Spatiotemporal Characterization of Muscle Activation Patterns from Vagus Nerve Stimulation in Human Subjects

Background and Hypothesis: Vagus nerve stimulation (VNS) is used for treatment of epilepsy in over 100,000 patients worldwide and is a potential treatment for many inflammatory disorders. VNS-evoked compound nerve action potentials (CNAPs) may have applications as a biomarker of VNS treatment efficacy, but transcutaneous measurement of this activity is obscured by muscle artifacts. A more precise understanding of muscle activation patterns could improve recording and analysis protocols for isolating the vagus nerve (VN) CNAP. We hypothesize that analyzing multi-electrode array (MEA) surface recordings overlying the VN will allow us to characterize this muscle artifact. 
 Project Methods: Five patients undergoing VNS therapy for epilepsy were enrolled in a clinical study at Indiana University School of Medicine (IRB #2006075899). A custom-made MEA with a grid of 32 soft foam electrodes was placed on the skin overlying the VN on each side of the neck. Surface potentials were recorded for approximately 20 minutes at the patient’s established device settings.
 Results: VNS-evoked potentials were visualized in all five patients. Two probable muscle artifacts were identified, defined as non-propagating features in the mean response to n > 600 pulses of VNS. The first had an onset latency of ~1-3 ms, and the second had a latency of ~7-10 ms. Both artifacts appeared primarily in the electrodes overlying the laryngeal muscles.
 Conclusion and Potential Impact: The short latency of the first muscle artifact suggests stimulus signal leakage activating the superior laryngeal nerve. The second artifact with longer latency is likely caused by the recurrent laryngeal nerve. Previous analyses may have mischaracterized the first muscle artifact as nerve activity. The use of MEA-based recordings clarifies our understanding of the VN’s response to VNS, which may lead to better treatment efficacy and the eventual development of personalized VNS therapies for epilepsy and a range of inflammatory disorders.

Grasshopper optimization algorithm based improved variational mode decomposition technique for muscle artifact removal in ECG using dynamic time warping

The removal of unwanted noise from electrocardiogram (ECG) recordings is a difficult procedure in biomedical signal analysis. It alters the signal and affects the accurate interpretation of the signal. A major distortion produced while recording is due to muscle artifact (MA) or electromyographic (EMG) noise. To achieve a high-quality ECG recording by eliminating this MA noise, our research proposes a novel filtering technique using the grasshopper optimization algorithm (GOA) based variational mode decomposition (VMD) method with the dynamic time warping (DTW) distance concept. GOA is utilized to identify the best optimal parameters for the VMD process, and then the DTW technique is applied to discover the relevant modes that include MA noise. To successfully remove noise, these modes are denoised using the discrete wavelet transform (DWT) method. ECG signal analysis is performed on a real-time MIT-BIH arrhythmia database and is compared with the performance of recent existing techniques using signal characteristics such as signal-to-noise ratio (SNR), mean square error (MSE), correlation coefficient (CC), and so on. As an outcome of these computations, our proposed technique outperforms those existing methods when it comes to denoising MA noise.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography.

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also revealed by our study, experts’ opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

Real-time filtering adaptive algorithms for non-stationary noise in electrocardiograms

A non-stationary noise with a previously unknown intensity level often contaminates electrocardiogram (ECG) signals. Therefore, provision of high quality suppression of the non-stationary noise in ECG is a vital task to be performed. A new lightweight adaptive method has been proposed for real-time filtering of non-stationary (from the point of view of its variance) noise in ECG with noise- and signal-dependent switching filters, appropriate for processing a local vicinity of the current input signal sample. This method does not require time for filter parameters adaptation and a priori information about the noise variance. A one- and a two-pass algorithm on the simple optimal Savitzky & Golay filters and on the linear averaging filter have been developed on the basis of the proposed method. There is also an algorithm suggested applying a re-filtering only when the identifiers used in the method define a not low noise level. The integral and local statistical estimates of filters' efficiency have been obtained from numerical simulations over mean-square error (MSE), maximum absolute error (MAE), and signal-to-noise ratio (SNR) for a model ECG signal under different levels of Gaussian noise. Filtering efficiency was estimated with the real signals taken from physionet.org database. The filter parameters were chosen by numerical simulations for a typical P-QRS-T cycle with corresponding signal sampling rate and scale considered. For a wide range from low to high noise levels (input SNR belongs to the interval from 25 to 0 dB), the statistical estimates of efficiency have been obtained as follows: for an ECG sampled at 360 Hz (taken from NSTDB), inside QRS-complex, the SNR increases by 2.5–6.7 dB, the MSE decreases in 1.7–4.3 times and the MAE decreases in 1.3–2.2 times; inside the segments prior to and following QRS-complex, the SNR, on an average, increase by 8.6–13.2 dB and the MSE decreases in 7.1–19.2 times, and the MAE decreases in 2.4–5.1 times. For an ECG sampled at 1 kHz (taken from PTB), inside QRS-complex, the SNR increases by 5.2–8 dB, the MSE and the MAE decrease in 3.2–6 times and 1.9–2.7 times, respectively; outside QRS, the SNR increases by 10.2–15.8 dB, the MSE and the MAE decrease in 10.5–36 times and in 2.6–5.2 times, respectively. For an ECG sampled at 250 Hz (from CUDB), the local indicators of efficiency are: inside QRS-complex, the SNR increases by 2.9–6.6 dB, the MSE and the MAE decrease in 1.8–4 times and in 1.4–2.3 times, respectively; outside QRS-complex, the SNR, on an average, increase by 4.7–11.6 dB, the MSE and the MAE decrease in 3.3–10.9 times and in1.7–3.9 times. Additionally, the filters' efficiency has been estimated as to suppression of real electromyographic (EMG) noise with significantly different variance and the proposed algorithms have been compared with other filters. A noise-free ECGs during 5 min sampled at 360 Hz were contaminated with highly non-stationary EMG noise from a muscle artifact (MA) record of different intensity (input SNR varies from 20 to −5 dB), the SNR improvement at the proposed algorithm output is 10–14 dB. The calculated quantitative estimates of efficiency confirm the high quality of non-stationary EMG noise suppression obtained with the adaptive algorithms suggested. Minute signal distortions and a high degree of noise suppression have been demonstrated. Good performance and high filter quality for various real signals with non-stationary EMG noise have been shown. ECG amplitude-time parameters and waveforms, including pathological changes, are shown to be well-preserved.

Overview of the Most Important Advances in Magnetoencephalography Technology

The overview of the latest advances in magnetoencephalography (MEG) technology, including the development of optically pumped magnetometers (OPM), is presented. The main advantage of OPM over conventional superconducting quantum interference devices (SQUID) is the absence of cryogenic cooling, which reduces the cost of equipment by 2-3 times. Moreover, the OPM can be positioned a few millimeters from the scalp, which roughly doubles the signal-to-noise ratio. In addition, they are not as susceptible to muscle artifacts as electroencephalography (EEG) signals. Moreover, placing the OPM in a nulling magnetic field reduces the effects of head movement artifacts in the surrounding field. All these advantages open up great potential for the development of a new generation of OPM-based brain-computer interfaces (BCIs) that are cheaper, more flexible and more responsive than SQUID-based BCIs, which can perform both motor and non-motor tasks. Despite the tremendous progress made over the past few years, OPM-MEG is still an evolving technology that requires further improvement. Due to the large size of the sensors, the number of channels is relatively small (less than 50), so they cannot cover the entire head. Although many BCI applications require only a few sensors, their correct placement in selected areas of the scalp is very important. The miniaturization and versatility of lightweight helmets could be an important step towards the further development of OPM for BCI and other applications.

Artefact subspace reconstruction for both EEG and fNIRS co-registred signals.

Combining electroencephalography (EEG) to functional near-infrared spectroscopy (fNIRS) is a promising technique that has gained momentum thanks to their complementarity. While EEG measures the electrical activity of the brain, fNIRS records the variations in cerebral blood flow and related hemoglobin concentrations. However, both modalities are typically contaminated with artefacts. Muscle and eye artefacts, affect the EEG signals, while hemodynamic and oxygenation changes in the extracerebral compartment due to systemic changes (superficial layer) corrupt the fNIRS signals. Moreover, both signals are sensitive to sensor motion artefacts characterized by large amplitude. There are several well-established methods for removing artefacts for both modalities. The objective of this paper is to apply a common approach to denoise both EEG and fNIRS signals. Indeed Artifact Subspace Reconstruction (ASR) method, which is an automatic, online-capable and efficient method for deleting transient or large-amplitude EEG artefacts, can be a good alternative to also denoise fNIRS signals. In this paper, we first propose, a new more comprehensive formulation of ASR. Then, we study the effectiveness of the method in denoising both the EEG and fNIRS signals.

Increased Entropy of Gamma Oscillations in the Frontal Region during Meditation.

Meditation practices are considered mental training and have increasingly received attention from the scientific community due to their potential psychological and physical health benefits. We compared the EEG data recorded from long-term rajayoga practitioners during different meditative and non-meditative periods. Minimum variance modified fuzzy entropy (MVMFE) is computed for each EEG band for all channels of a given lobe. The means across all the channel entropy values were obtained and compared during meditative and non-meditative states. Meditators showed higher frontal entropy in the lower gamma band (25-45Hz) during the meditative states. Independent component analysis was applied to ensure that muscle or eye artifacts did not contribute to the gamma activity. Our results extend previous findings on the changes in entropy observed in long-term meditators during rajayoga practice. Gamma band in EEG is implicated in cognitive processes requiring high-level processing such as attention, learning, memory control, and retrieval. Gamma activity is also suggested as a potential biomarker for therapeutic progress in patients with clinical depression. Based on our findings, there is an excellent possibility to utilize the practice of meditation as a training tool to strengthen the neural circuits, where age-related degeneration is making its pathological impact.

The use of optically pumped Magnetoencephalography (OP-MEG) in epilepsy

Magnetoencephalography (MEG) has been used in specific cases of refractory epilepsy due to its inherent properties of excellent spatial resolution and relative immunity to muscle artefact. However current MEG systems are high maintenance and restrictive for the patient, meaning that recording sessions are usually brief (1–2 h), thereby limiting its availability in the clinical setting. Our aim was to demonstrate the first use of Optically Pumped Magnetoencephalography (OP-MEG) in two epilepsy patients with unrestricted head movement. Unlike conventional MEG that uses a superconducting SQUID system, here we use a different type of sensor (OPM), which operates at room temperature and crucially can be placed directly on the patient's scalp, permitting free head movement.

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition.

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

Seizures induced in electroconvulsive therapy as a human epilepsy model: A comparative case study.

ObjectiveStandardized investigation of epileptic seizures and the postictal state may contribute to a better understanding of ictal and postictal phenomena. This comparative case study aims to assess whether electrically induced seizures in electroconvulsive therapy (ECT) show sufficient similarities with spontaneous seizures to serve as a human epilepsy model.MethodsWe compared six EEG recordings, three ECT‐induced seizures and three generalized tonic‐clonic seizures, using quantitative electroencephalography (EEG) analyses. EEG recordings during and after ECT sessions (under general anesthesia and muscle paralysis) were collected prospectively, whereas epilepsy data were selected retrospectively. Time‐frequency representations, dominant ictal frequencies, and postictal alpha‐delta ratios were calculated.ResultsIn all EEG recordings, a decrease in dominant ictal frequency was observed, as well as postictal suppression. Postictal alpha‐delta ratio indicated the same trend for all: a gradual increase from predominantly delta to alpha frequencies on timescales of hours after the seizure. Postictal spectral representation was similar. Muscle artifacts were absent in ECT‐induced seizures and present in spontaneous seizures. Ictal amplitude was higher in epileptic than in ECT‐induced seizures. Temporospectral ictal dynamics varied slightly between groups.SignificanceWe show that ictal and postictal characteristics in ECT and patients with generalized tonic‐clonic seizures are essentially similar. ECT‐induced seizures may be used to investigate aspects of ictal and postictal states in a highly predictable manner and well‐controlled environment. This suggests that clinical and electrophysiological observations during ECT may be extrapolated to epilepsy with generalized tonic‐clonic seizures.

An Adaptive QRS Detection Algorithm with Variable Threshold. (Dept. E)

Ambulatory electrocardiogram ( ECG ) recordings from patients undergoing severe physical stress are corrupted by large muscle artifacts ( EMG ) and other noise sources, such as baseline drifts and currents induced by motion artifacts and electromechanical devices. A simple real-time algorithm has been developed which is suitable for on-line detection of the QRS complexes. The algorithm calculates several statistical parameters of the ECG signal which are used to adapt the threshold to baseline changes in the signal, thereby increasing detection sensitivity.

A novel method to reduce false alarms in ECG diagnostic systems: capture and quantification of noisy signals

Objective. Muscle artifacts (MA) and electrode motion artifacts (EMA) in electrocardiogram (ECG) signals lead to a large number of false alarms from cardiac diagnostic systems. To reduce false alarms, it is necessary to improve the performance of the diagnostic algorithm in noisy environments by removing excessively noisy signals. However, existing methods focus on signal quality assessment and contain too many artificial features. Here, we present a novel method to flexibly eliminate noisy signals without any artificial features. Approach. Our method contains an improved lightweight deep neural network (DNN) to capture the signal portions damaged by EMA and MA, uses the sample entropy to quantize noisy portions, and discards those portions that exceed a defined threshold. Our method was tested in conjunction with Pan-Tompkins (PT), Filter Bank (FB), and ‘UNSW’ R-peak detection algorithms along with two heartbeat classification algorithms on datasets synthesized from the MIT-BIH Noise Stress Test Database, the China Physiological Signal Challenge 2018 Database and the MIT-BIH Arrhythmia Database. Main results. For PT R-peak detection algorithms, the sensitivity (Se) increased noticeably from 89.01% to 99.42% in the synthesized datasets with a signal-to-noise ratio of 6 dB. With the same datasets, the Se of the FB algorithm increased about 9.29%, and a 3.64% increase occurred in the Se of the ‘UNSW’ algorithm. For heartbeat classification algorithms, the overall F1-score increased about 6% in the synthesized one-heartbeat datasets. It is the first study to utilize a DNN to capture noisy segments of the ECG signal. Significance. Too many false alarms can cause alarm fatigue. Our method can be utilized as the preprocessing before signal analysis, thereby reducing false alarms from the ECG diagnostic systems.

A denoising performance comparison based on ECG Signal Decomposition and local means filtering

The electrocardiogram (ECG) signal is popular and extensively used as a diagnostic tool for revealing several diseases related to the heart. Unfortunately, its sensitive to noises from various sources. Denoising these noises remains a hard and major challenge task, although there are some existing methods for denoising, they remain limited to a few types of noises with significant distortion after the filtering process. In this paper, we investigated a hybrid system that can deal with different types of noises, with different levels of SNR, conserving all the signal features with the minimum distortion. The hybrid system consists of a decomposition method followed by local means (LM) filtering. The best candidates used for the signal decomposition are the Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). To demonstrate the effectiveness of the proposed method, several real ECG recordings and synthetic ECG infected by a different type of noise (i.e., baseline wander, muscle artifacts, electrode motion, mix noise, white noise, colored noise) with different levels of signal to noise ratios (24, 18, 6, 0, −6) dB are used in our numerical analysis. The performance evaluation of our proposed system is made using the following metrics: SNR improvement, signal-to-noise and distortion ratio (SINAD), the approximate entropy, and the fuzzy entropy. The experimental results emphasized that the Ensemble Empirical Mode Decomposition (EEMD) followed by local means (LM) outperforms all the comparison methods, in terms of all the metrics used, with few exceptions where the Empirical Mode Decomposition (EMD) followed by local means (LM) and Discrete Wavelet Transform (DWT) followed by local means (LM) performed well.

Wielding and evaluating the removal composition of common artefacts in EEG signals for driving behaviour analysis

Noninvasive EEG signals provide neural activity information at high resolution, of which human mental status can be properly detected. However, artefacts always exist in brain oscillatory EEG signals and thus impede the accuracy and reliability of relevant analysis, especially in real-world tasks. Moreover, the use of a mature artefact identification method cannot assure impeccable artefact separation; this leads to a trade-off between removing contaminated information and losing valuable information. This study addresses this problem by investigating a simulator-based driving behaviour analysis using a car-following scenario to correlate the EEG-based mental features with behavioural responses. The study develops an architecture for an artefact composition pool and proposes three integrated prediction models to evaluate the removal compositions of the EEG artefacts. Three errors (mean absolute, root mean square, mean absolute percentage) and R-squared index are considered for measuring the performance of the models. The results show that the best-performing composition outperformed the no-removal and all-removal cases by 11.75% and 4.28% improvements, respectively. Specifically, we investigate different common artefacts including eye blinks, horizontal eye movements, vertical eye movements, generic discontinuities and muscle artefacts. The gained knowledge on artefact removal, EEG spectral features and stimuli-response patterns can be further applied to properly manipulate real-world EEG signals and develop an effective brain-computer interface.

Minimizing Biosignal Recording Sites for Noninvasive Hybrid Brain/Neural Control

Noninvasive brain/neural controlled robots are promising tools to improve autonomy and quality of life in severe paralysis, but require biosignal recordings, such as electroencephalography (EEG) and electrooculography (EOG), from various sites distributed over the user's head. This limits the applicability and practicality of noninvasive brain/neural robot control on an everyday basis. It would thus be very desirable to minimize the number of necessary recording sites paving the way for miniaturized, headset-like EEG/EOG systems that users with hemiplegia can mount by themselves. Here, we introduce a novel EEG/EOG brain/neural control strategy using only scalp electrodes placed near cortical sensorimotor areas. The strategy was tested across 16 healthy volunteers who engaged in an EEG/EOG brain/neural control task. Classification accuracies were compared using scalp electrodes only versus the conventional electrode placements across the scalp and face. To evaluate whether cranial muscle artifacts impede classification accuracy, participants were asked to clench their teeth during the task. We found that brain/neural classification accuracy was comparable and that clenching had no impact on classification accuracy across both conditions. Our results suggest that the proposed new strategy allows for reliable EEG/EOG-based brain/neural control, a critical prerequisite to broaden the use of noninvasive brain/neural assistive and rehabilitative technologies.

Electrocardiogram signals denoising using improved variational mode decomposition

Background:Electrocardiogram (ECG) plays a vital role in the analysis of heart activity. It can be used to analyze the different heart diseases and mental stress assessment also. Various noises, such as baseline wandering, muscle artifacts and power line interface disturbs the information within the ECG signal. To acquire correct information from ECG signal, these noises should be removed.Methods:In the proposed work, the improved variational mode decomposition (IVMD) method for the removal of noise in ECG signals is used. In the proposed method, the weighted signal amplitude integrated over the timeframe of the ECG signal varies the window size during decomposition. Raw ECG data are extracted from 10 subjects and ECG data are also taken from the MIT BIH database for the proposed method.Results:The performance comparison of traditional variational mode decomposition (VMD) and the proposed technique is also calculated using mean square error, percentage root mean square difference, signal to noise ratio and correlation coefficient. The extracted highest signal to noise ratio (SNR) value of acquired ECG signals using traditional VMD is 42db whereas highest value of signal to noise ratio (SNR) using improved VMD (IVMD) is 83db.Conclusion:The proposed IVMD technique represented better performance than traditional VMD for denoising of ECG signals.