Study and analysis of motion artifacts for ambulatory electroencephalography

Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings.

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Improving the quality of combined EEG-TMS neural recordings: Introducing the Coil Spacer

10.1101/170431 ◽

2017 ◽

Author(s):

K.L. Ruddy ◽

D.G. Woolley ◽

D. Mantini ◽

J.H. Balsters ◽

N. Enz ◽

...

Keyword(s):

Direct Contact ◽

Best Practice ◽

Low Frequency ◽

High Amplitude ◽

Neural Recordings ◽

Standard Data ◽

Brain Signals ◽

Eeg Data ◽

Theta Frequency ◽

Eeg Recordings

ABSTRACTBackgroundIn the last decade, interest in combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) approaches has grown substantially. Aside from the obvious artifacts induced by the magnetic pulses themselves, separate and more sinister signal disturbances arise as a result of contact between the TMS coil and EEG electrodes.New methodHere we profile the characteristics of these artifacts and introduce a simple device – the coil spacer - to provide a platform allowing physical separation between the coil and electrodes during stimulation.ResultsEEG data revealed high amplitude signal disturbances when the TMS coil was in direct contact with the EEG electrodes, well within the physiological range of viable EEG signals. The largest artifacts were located in the Delta and Theta frequency range, and standard data cleanup using independent components analysis (ICA) was ineffective due to the artifact’s similarity to real brain oscillations.Comparison with Existing MethodWhile the current best practice is to use a large coil holding apparatus to fixate the coil ‘hovering’ over the head with an air gap, the spacer provides a simpler solution that ensures this distance is kept constant throughout testing.ConclusionsThe results strongly suggest that data collected from combined TMS-EEG studies with the coil in direct contact with the EEG cap are polluted with low frequency artifacts that are indiscernible from physiological brain signals. The coil spacer provides a cheap and simple solution to this problem and is recommended for use in future simultaneous TMS-EEG recordings.

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Wavelet Domain Optimized Savitzky–Golay Filter for the Removal of Motion Artifacts From EEG Recordings

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2020.3041099 ◽

2021 ◽

Vol 70 ◽

pp. 1-11

Author(s):

Pranjali Gajbhiye ◽

Nopparada Mingchinda ◽

Wei Chen ◽

Subhas Chandra Mukhopadhyay ◽

Theerawit Wilaiprasitporn ◽

...

Keyword(s):

Motion Artifacts ◽

Wavelet Domain ◽

Eeg Recordings

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An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Sensors ◽

10.3390/s18113721 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3721 ◽

Cited By ~ 17

Author(s):

Usman Rashid ◽

Imran Niazi ◽

Nada Signal ◽

Denise Taylor

Keyword(s):

Experimental Study ◽

Mixed Models ◽

Signal To Noise Ratio ◽

Low Cost ◽

Low Frequency ◽

Low Power Consumption ◽

Motor Tasks ◽

Negative Peak ◽

Eeg Data ◽

Healthy Participants

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

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Signal-space projection suppresses the tACS artifact in EEG recordings

10.1101/823153 ◽

2019 ◽

Cited By ~ 3

Author(s):

Johannes Vosskuhl ◽

Tuomas P. Mutanen ◽

Toralf Neuling ◽

Risto J. Ilmoniemi ◽

Christoph S. Herrmann

Keyword(s):

Brain Activity ◽

Current Source ◽

Oscillatory Activity ◽

Signal Space ◽

Artifact Correction ◽

Eeg Data ◽

Before And After ◽

Transcranial Alternating Current Stimulation ◽

Space Projection ◽

Eeg Recordings

1.AbstractBackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the huge tACS-caused artifact in electroencephalography (EEG) signals, tACS–EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on a human subject to validate the best-performing data-correction approach.ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.Comparison with existing methodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction.ConclusionsOur results demonstrate the feasibility of SSP by applying it to human tACS–EEG data.

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Use of Transforms in Biomedical Signal Processing and Analysis

10.5772/intechopen.98239 ◽

2021 ◽

Author(s):

Ette Harikrishna ◽

Komalla Ashoka Reddy

Keyword(s):

Wavelet Transforms ◽

Research Work ◽

Reference Signal ◽

Low Frequency ◽

Motion Artifact ◽

Motion Artifacts ◽

Biomedical Signal Processing ◽

Blood Oxygen Saturation ◽

Biomedical Signals ◽

Electrocardiogram Ecg

Biomedical signals like electrocardiogram (ECG), photoplethysmographic (PPG) and blood pressure were very low frequency signals and need to be processed for further diagnosis and clinical monitoring. Transforms like Fourier transform (FT) and Wavelet transform (WT) were extensively used in literature for processing and analysis. In my research work, Fourier and wavelet transforms were utilized to reduce motion artifacts from PPG signals so as to produce correct blood oxygen saturation (SpO2) values. In an important contribution we utilized FT for generation of reference signal for adaptive filter based motion artifact reduction eliminating additional sensor for acquisition of reference signal. Similarly we utilized the transforms for other biomedical signals.

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Review on the wearable health-care monitoring system with robust motion artifacts reduction techniques

Sensor Review ◽

10.1108/sr-05-2021-0150 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Aarthy Prabakaran ◽

Elizabeth Rufus

Keyword(s):

Health Care ◽

Monitoring System ◽

Learning Algorithm ◽

Motion Artifacts ◽

Sensor Data ◽

Monitoring Systems ◽

Design Modification ◽

Content Type ◽

Wearable Monitoring Systems ◽

Health Care Monitoring

Purpose Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users. Design/methodology/approach MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored. Findings According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems. Originality/value This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

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Artifacts

MEG-EEG Primer ◽

10.1093/med/9780190497774.003.0008 ◽

2017 ◽

pp. 98-127

Author(s):

Riitta Hari ◽

Aina Puce

Keyword(s):

Data Analysis ◽

Eye Movements ◽

Cardiac Activity ◽

Signal Space Separation ◽

Eeg Data ◽

Eeg Recordings ◽

Power Line Noise ◽

Post Hoc ◽

Space Separation ◽

The Brain

This chapter focuses on different types of biological and nonbiological artifacts in MEG and EEG recordings, and discusses methods for their recognition and removal. Examples are given of various physiological artifacts, including eye movements, eyeblinks, saccades, muscle, and cardiac activity. Nonbiological artifacts, such as power-line noise, are also demonstrated. Some examples are given to illustrate how these unwanted signals can be identified and removed from MEG and EEG signals with methods such as independent component analysis (as applied to EEG data) and temporal signal-space separation (applied to MEG data). However, prevention of artifacts is always preferable to removing or compensating for them post hoc during data analysis. The chapter concludes with a discussion of how to ensure that signals are emanating from the brain and not from other sources.

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Amplitude of fNIRS Resting-State Global Signal Is Related to EEG Vigilance Measures: A Simultaneous fNIRS and EEG Study

Frontiers in Neuroscience ◽

10.3389/fnins.2020.560878 ◽

2020 ◽

Vol 14 ◽

Author(s):

Yuxuan Chen ◽

Julia Tang ◽

Yafen Chen ◽

Jesse Farrand ◽

Melissa A. Craft ◽

...

Keyword(s):

Human Brain ◽

Resting State ◽

Near Infrared ◽

Low Frequency ◽

Temporal Correlation ◽

Frequency Range ◽

Functional Near Infrared Spectroscopy ◽

Physical Constraints ◽

Global Signal ◽

Eeg Data

Recently, functional near-infrared spectroscopy (fNIRS) has been utilized to image the hemodynamic activities and connectivity in the human brain. With the advantage of economic efficiency, portability, and fewer physical constraints, fNIRS enables studying of the human brain at versatile environment and various body positions, including at bed side and during exercise, which complements the use of functional magnetic resonance imaging (fMRI). However, like fMRI, fNIRS imaging can be influenced by the presence of a strong global component. Yet, the nature of the global signal in fNIRS has not been established. In this study, we investigated the relationship between fNIRS global signal and electroencephalogram (EEG) vigilance using simultaneous recordings in resting healthy subjects in high-density and whole-head montage. In Experiment 1, data were acquired at supine, sitting, and standing positions. Results found that the factor of body positions significantly affected the amplitude of the resting-state fNIRS global signal, prominently in the frequency range of 0.05–0.1 Hz but not in the very low frequency range of less than 0.05 Hz. As a control, the task-induced fNIRS or EEG responses to auditory stimuli did not differ across body positions. However, EEG vigilance plays a modulatory role in the fNIRS signals in the frequency range of less than 0.05 Hz: resting-state sessions of low EEG vigilance measures are associated with high amplitudes of fNIRS global signals. Moreover, in Experiment 2, we further examined the epoch-to-epoch fluctuations in concurrent fNIRS and EEG data acquired from a separate group of subjects and found a negative temporal correlation between EEG vigilance measures and fNIRS global signal amplitudes. Our study for the first time revealed that vigilance as a neurophysiological factor modulates the resting-state dynamics of fNIRS, which have important implications for understanding and processing the noises in fNIRS signals.

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A step forward in the quest for a mobile EEG-designed epoch for psychophysiological studies

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2017-0189 ◽

2019 ◽

Vol 64 (6) ◽

pp. 655-667 ◽

Cited By ~ 1

Author(s):

Sebastián A. Balart-Sánchez ◽

Hugo Vélez-Pérez ◽

Sergio Rivera-Tello ◽

Fabiola R. Gómez Velázquez ◽

Andrés A. González-Garrido ◽

...

Keyword(s):

Comparative Analysis ◽

International System ◽

Central Area ◽

Behavioral Responses ◽

Clinical Grade ◽

Healthy Individuals ◽

Eeg Data ◽

Wide Range ◽

Eeg Recordings ◽

Visual Oddball

Abstract The aim of this study was to compare a reconfigurable mobile electroencephalography (EEG) system (M-EMOTIV) based on the Emotiv Epoc® (which has the ability to record up to 14 electrode sites in the 10/20 International System) and a commercial, clinical-grade EEG system (Neuronic MEDICID-05®), and then validate the rationale and accuracy of recordings obtained with the prototype proposed. In this approach, an Emotiv Epoc® was modified to enable it to record in the parieto-central area. All subjects (15 healthy individuals) performed a visual oddball task while connected to both devices to obtain electrophysiological data and behavioral responses for comparative analysis. A Pearson’s correlation analysis revealed a good between-devices correlation with respect to electrophysiological measures. The present study not only corroborates previous reports on the ability of the Emotiv Epoc® to suitably record EEG data but presents an alternative device that allows the study of a wide range of psychophysiological experiments with simultaneous behavioral and mobile EEG recordings.

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PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation

Sensors ◽

10.3390/s20061573 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1573 ◽

Cited By ~ 1

Author(s):

Haojie Liu ◽

Kang Liao ◽

Chunyu Lin ◽

Yao Zhao ◽

Meiqin Liu

Keyword(s):

Point Cloud ◽

Spatial Information ◽

Interpolation Method ◽

Low Frequency ◽

Point Clouds ◽

Autonomous Driving ◽

Sensor Data ◽

Navigation Systems ◽

Intermediate Point ◽

Cascade Structure

LiDAR sensors can provide dependable 3D spatial information at a low frequency (around 10 Hz) and have been widely applied in the field of autonomous driving and unmanned aerial vehicle (UAV). However, the camera with a higher frequency (around 20 Hz) has to be decreased so as to match with LiDAR in a multi-sensor system. In this paper, we propose a novel Pseudo-LiDAR interpolation network (PLIN) to increase the frequency of LiDAR sensor data. PLIN can generate temporally and spatially high-quality point cloud sequences to match the high frequency of cameras. To achieve this goal, we design a coarse interpolation stage guided by consecutive sparse depth maps and motion relationship. We also propose a refined interpolation stage guided by the realistic scene. Using this coarse-to-fine cascade structure, our method can progressively perceive multi-modal information and generate accurate intermediate point clouds. To the best of our knowledge, this is the first deep framework for Pseudo-LiDAR point cloud interpolation, which shows appealing applications in navigation systems equipped with LiDAR and cameras. Experimental results demonstrate that PLIN achieves promising performance on the KITTI dataset, significantly outperforming the traditional interpolation method and the state-of-the-art video interpolation technique.

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