A Novel Technique to Reject Artifact Components for Surface EMG Signals Recorded During Walking With Transcutaneous Spinal Cord Stimulation: A Pilot Study.
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SUBMITTER: Kim M
PROVIDER: S-EPMC8209256 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundArtifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts.MethodsWe propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects.ResultsBased on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components.ConclusionsWe propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.
| S-EPMC3175453 | biostudies-literature
Project description:Objective.Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR).Approach.The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs);n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database.Main results.We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data.Significance.APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.
| S-EPMC10696919 | biostudies-literature
Project description:The appreciation for the need to record electroencephalographic (EEG) signals from humans while walking has been steadily growing in recent years, particularly in relation to understanding gait disturbances. Movement artefacts (MA) in EEG signals originate from mechanical forces applied to the scalp electrodes, inducing small electrode movements relative to the scalp which, in turn, cause the recorded voltage to change irrespectively of cortical activity. These mechanical forces, and thus MA, may have various sources (e.g., ground reaction forces, head movements, etc.) that are inherent to daily activities, notably walking. In this paper we introduce a systematic, integrated methodology for removing MA from EEG signals recorded during treadmill (TM) and over-ground (OG) walking, as well as quantify the prevalence of MA in different locomotion settings. In our experiments, participants performed walking trials at various speeds both OG and on a TM while wearing a 32-channel EEG cap and a 3-axis accelerometer, placed on the forehead. Data preprocessing included separating the EEG signals into statistically independent additive components using independent component analysis (ICA). We observed an increase in electro-physiological signals (e.g., neck EMG activations for stabilizing the head during heel-strikes) as the walking speed increased. These artefact independent-components (ICs), while not originating from electrode movement, still exhibit a similar spectral pattern to the MA ICs-a peak at the stepping frequency. MA was identified and quantified in each component using a novel method that utilizes the participant's stepping frequency, derived from a forehead-mounted accelerometer. We then benchmarked the EEG data by applying newly established metrics to quantify the success of our method in cleaning the data. The results indicate that our approach can be successfully applied to EEG data recorded during TM and OG walking, and is offered as a unified methodology for MA removal from EEG collected during gait trials.
| S-EPMC5955591 | biostudies-literature
Project description:(1) Background: The purpose of this study was to evaluate the analysis of measurements of bioelectric signals obtained from electromyographic sensors. A system that controls the speed and direction of rotation of a brushless DC motor (BLDC) was developed; (2) Methods: The system was designed and constructed for the acquisition and processing of differential muscle signals. Basic information for the development of the EMG signal processing system was also provided. A controller system implementing the algorithm necessary to control the speed and direction of rotation of the drive rotor was proposed; (3) Results: Using two muscle groups (biceps brachii and triceps), it was possible to control the direction and speed of rotation of the drive unit. The control system changed the rotational speed of the brushless motor with a delay of about 0.5 s in relation to the registered EMG signal amplitude change; (4) Conclusions: The prepared system meets all the design assumptions. In addition, it is scalable and allows users to adjust the signal level. Our designed system can be implemented for rehabilitation, and in exoskeletons or prostheses.
| S-EPMC9504870 | biostudies-literature
Project description:Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain's electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labelled data are publicly available. This paper presents a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilised to develop independent component classifiers.
| S-EPMC9392781 | biostudies-literature
Project description:BackgroundIn concurrent EEG/fMRI recordings, EEG data are impaired by the fMRI gradient artifacts which exceed the EEG signal by several orders of magnitude. While several algorithms exist to correct the EEG data, these algorithms lack the flexibility to either leave out or add new steps. The here presented open-source MATLAB toolbox FACET is a modular toolbox for the fast and flexible correction and evaluation of imaging artifacts from concurrently recorded EEG datasets. It consists of an Analysis, a Correction and an Evaluation framework allowing the user to choose from different artifact correction methods with various pre- and post-processing steps to form flexible combinations. The quality of the chosen correction approach can then be evaluated and compared to different settings.ResultsFACET was evaluated on a dataset provided with the FMRIB plugin for EEGLAB using two different correction approaches: Averaged Artifact Subtraction (AAS, Allen et al., NeuroImage 12(2):230-239, 2000) and the FMRI Artifact Slice Template Removal (FASTR, Niazy et al., NeuroImage 28(3):720-737, 2005). Evaluation of the obtained results were compared to the FASTR algorithm implemented in the EEGLAB plugin FMRIB. No differences were found between the FACET implementation of FASTR and the original algorithm across all gradient artifact relevant performance indices.ConclusionThe FACET toolbox not only provides facilities for all three modalities: data analysis, artifact correction as well as evaluation and documentation of the results but it also offers an easily extendable framework for development and evaluation of new approaches.
| S-EPMC3840732 | biostudies-literature
Project description:With the increase in GNSS user base, the studies of threats and vulnerabilities of GNSS system are also increased. Among the threats, spoofing is of particular interest because of the risk associated with it. The studies on spoofing are generally limited to simulated scenarios as the real world spoofing attack is very difficult to create or spot and record for analysis. This paper presents a method of generating baseband spoofing data using real world signals by simultaneous recordings of GNSS signals using two separate receivers, where one of them simulates the receiver under attack and the other simulates the response the spoofer will be going to produce to fabricate the attack. After taking the records and merging them to create the spoofing baseband signals, it is checked against several spoofing detection methods to verify the valid spoofing attack being present in the signal. This method produces the signal recordings that have real world disturbances in it that may be difficult to simulate. The developed method has the following advantages: •It does not require very expensive hardware to produce an intermediate spoofing signal.•The user has control over the spoofing power advantage.•The same scenario can be reproduced with varying parameters.
| S-EPMC6197700 | biostudies-literature
Project description:Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to limited understanding of how a patient's lower extremity muscle excitations contribute to the patient's lower extremity joint moments. To assist in the study of these disorders, researchers have developed electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal geometry. While these models can predict individual muscle contributions to lower extremity joint moments during walking, the accuracy of the predictions can be hindered by errors in the scaled geometry. This study presents a novel EMG-driven modeling method that automatically adjusts surrogate representations of the patient's musculoskeletal geometry to improve prediction of lower extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, and moment arms. We evaluated our EMG-driven modeling method using data collected from a high-functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled musculoskeletal model. We found that our EMG-driven modeling method incorporating automated adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately than did the same method without geometric adjustments. Geometric adjustments improved moment prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature between scaled generic geometric models and measurements made from imaging data. Our results demonstrate that with appropriate experimental data, joint moment predictions for walking generated by an EMG-driven model can be improved significantly when automated adjustment of musculoskeletal geometry is included in the model calibration process.
| S-EPMC5507406 | biostudies-literature
Project description:ObjectiveTo evaluate the impact of using transcutaneous electrical spinal cord stimulation (TSCS) on upper and lower extremity function in individuals with chronic spinal cord injury (SCI).DesignProspective case series.SettingSCI specific rehabilitation hospital.ParticipantsA convenience sample (N = 7) of individuals with tetraplegia who had previously been discharged from outpatient therapy due to a plateau in progress.InterventionsIndividuals participated in 60 min of upper extremity (UE) functional task-specific practice (FTP) in combination with TSCS and 60 min of locomotor training in combination with TSCS 5x/week.Main outcome measuresThe primary outcome for this analysis was the Capabilities of Upper Extremity Test (CUE-T). Secondary outcomes include UE motor score (UEMS), LE motor score (LEMS), sensation (light touch and pin prick), Nine-Hole Peg Test, 10 meter walk test, 6 min walk test, and 5 min stand test.ResultsSeven individuals (four motor complete; three motor incomplete) completed 20-80 sessions UE and LE training augmented with TSCS and without any serious adverse events. Improvements were reported on the CUE-T in all seven individuals. Two individuals improved their ASIA impairment scale (AIS) classification (B to C; C to D) and two individuals improved their neurologic level of injury by one level (C4-C5; C5-C6). Sensation improved in five individuals and all four who started out with motor complete SCIs were able to voluntarily activate their LEs on command in the presence of stimulation.ConclusionIndividuals with chronic SCI who had previously demonstrated a plateau in function after an intensive outpatient therapy program were able to improve in a variety of UE and LE outcomes in response to TSCS without any adverse events. This was a small pilot study and future fully powered studies with comparative interventions need to be completed to assess efficacy.
| S-EPMC9396932 | biostudies-literature
Project description:Background:Several 2-dimensional and 3-dimensional surfaces are available for cementless acetabular fixation. Plain radiographs are used to assess osseointegration; however, the radiographs are limited by their inability to capture the bone fixation process occurring over the 3-dimensional cup surface. In this cadaveric study, we compared the bone apposition between 2-dimensional and 3-dimensional cups. Methods:Both types of cups were implanted in 6 cadavers and pelvic radiographs obtained. Each cup was resected from the pelvis with adequate bone around it, and subsequently embedded in a polymer. Six sections of each cup were obtained to examine the metal and bone interface. Photographs and contact radiograph images were obtained for each section, and these were graded to arrive at percent metal-bone contact values for the cups. Results:On average, <30% of the cups' areas displayed radiolucencies on the pelvic radiographs for both cup types. For the section images and radiographs, there was about 80% aggregate contact between the cups and surrounding bone in both cup types. In the 3-dimensional cups group, some inconsistencies were found between the section photographs and the corresponding radiograph images. The radiolucencies observed on the section radiograph could not always be correlated with metal to bone gap on the section photograph. Conclusions:Good metal-bone contact (75% + contact area) was observed on both cup types. The inconsistencies found in the 3-dimensional cup group may be because of the interaction of radiographs with the unique porous cup surface resulting in artifactual radiolucencies.
| S-EPMC5712042 | biostudies-literature