scholarly journals Movement artefact removal from NIRS signal using multi-channel IMU data

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Masudur R. Siddiquee ◽  
J. Sebastian Marquez ◽  
Roozbeh Atri ◽  
Rodrigo Ramon ◽  
Robin Perry Mayrand ◽  
...  
Keyword(s):  
Movement Artefact ◽  
Artefact Removal ◽  
Nirs Signal

Psychophysiological Responses to Stress Related to Anxiety in Healthy Aging

2019 ◽  
Vol 33 (3) ◽  
pp. 188-197 ◽  
Author(s):  
Roberta Adorni ◽  
Agostino Brugnera ◽  
Alessia Gatti ◽  
Giorgio A. Tasca ◽  
Kaoru Sakatani ◽  
...  
Keyword(s):  
Healthy Aging ◽  
Near Infrared ◽  
Response Times ◽  
Cognitive Resources ◽  
Stressful Condition ◽  
Healthy Elderly ◽  
Situational Stress ◽  
Anxiety Response ◽  
Nirs Signal ◽  
Psychophysiological Correlates

Abstract. The aim of the study was to explore the effects of situational stress and anxiety in a group of healthy elderly, both in terms of psychophysiological correlates and cognitive performance. Eighteen participants ( Mage = 70 ± 6.3; range 60–85) were assessed for anxiety and were instructed to perform a computerized math task, under both a stressful and a control condition, while near-infrared spectroscopy (NIRS) signal and electrocardiography (ECG) were recorded. NIRS results evidenced an increased activation of right PFC during the entire procedure, even if effect sizes between left and right channels were larger during the experimental condition. The amount of right activation during the stressful condition was positively correlated with anxiety. Response times (RTs) were slower in more anxious than in less anxious individuals, both during the control and stressful conditions. Accuracy was lower in more anxious than in less anxious individuals, only during the stressful condition. Moreover, heart rate (HR) was not modulated by situational stress, nor by anxiety. Overall, the present study suggests that in healthy elderly, anxiety level has a significant impact on cerebral responses, and both on the amount of cognitive resources and the quality of performance in stressful situations.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

scholarly journals Characterizing Cochlear Implant Artefact Removal from EEG Recordings Using a Real Human Model

MethodsX ◽  
2021 ◽  
pp. 101369
Author(s):  
Jaime A. Undurraga ◽  
Lindsey Van Yper ◽  
Manohar Bance ◽  
David McAlpine ◽  
Deborah Vickers
Keyword(s):  
Cochlear Implant ◽  
Human Model ◽  
Artefact Removal ◽  
Eeg Recordings

scholarly journals A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI

NeuroImage ◽  
2007 ◽  
Vol 38 (1) ◽  
pp. 124-137 ◽  
Author(s):  
Frédéric Grouiller ◽  
Laurent Vercueil ◽  
Alexandre Krainik ◽  
Christoph Segebarth ◽  
Philippe Kahane ◽  
...  
Keyword(s):  
Comparative Study ◽  
Functional Mri ◽  
Eeg Signals ◽  
Artefact Removal

scholarly journals Learning MRI artefact removal with unpaired data

2021 ◽  
Vol 3 (1) ◽  
pp. 60-67
Author(s):  
Siyuan Liu ◽  
Kim-Han Thung ◽  
Liangqiong Qu ◽  
Weili Lin ◽  
Dinggang Shen ◽  
...  
Keyword(s):  
Artefact Removal

Quantifying the cortical contribution to the NIRS signal using simultaneous NIRS-BOLD measurements

2012 ◽  
Author(s):  
Louis Gagnon ◽  
Meryem A. Yücel ◽  
Mathieu Dehaes ◽  
Robert J. Cooper ◽  
Katherine L. Perdue ◽  
...  
Keyword(s):  
Nirs Signal

Investigation of EEG Correlate in NIRS Signal for BCI

2020 ◽  
pp. 319-325
Author(s):  
Ahmed Husnain Johar ◽  
Talha Yousaf ◽  
Umer Asgher ◽  
Yasar Ayaz ◽  
Salman Nazir ◽  
...  
Keyword(s):  
Nirs Signal

scholarly journals A Novel Tool for the Removal of Muscle Artefacts from EEG: Improving Data Quality in the Gamma Frequency Range

2020 ◽  
Author(s):  
Alina Pauline Liebisch ◽  
Thomas Eggert ◽  
Alina Shindy ◽  
Elia Valentini ◽  
Stephanie Irving ◽  
...  
Keyword(s):  
Data Quality ◽  
Time Course ◽  
Muscular Activity ◽  
Gamma Oscillations ◽  
Gamma Band ◽  
Gamma Activity ◽  
Single Muscle ◽  
Frequency Range ◽  
Continuous Eeg ◽  
Artefact Removal

AbstractBackgroundThe past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity.New MethodWe developed a novel approach to remove muscle artefacts from neurophysiological data. We re-analysed existing EEG data that were decomposed by a blind source separation method (independent component analysis, ICA), which helped to better spatially and temporally separate single muscle spikes. We then applied an adapting algorithm that detects these singled-out muscle spikes.ResultsWe obtained data almost free from muscle artefacts; we needed to remove significantly fewer artefact components from the ICA and we included more trials for the statistical analysis compared to standard ICA artefact removal. All pain-related cortical effects in the gamma band have been preserved, which underlines the high efficacy and precision of this algorithm.ConclusionsOur results show a significant improvement of data quality by preserving task-relevant gamma oscillations of cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures. We advocate the application of the tool for studies investigating gamma activity that contain a rather low number of trials, as well as for data that are highly contaminated with muscle artefacts. This validation of our tool allows for the application on event-free continuous EEG, for which the artefact removal is more challenging.

scholarly journals Artefact Removal from EEG Signals using Total Variation De-noising

2020 ◽  
Vol 9 (5) ◽  
pp. 2357-2361
Keyword(s):  
Root Mean Square Error ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Health Issues ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Medical Practitioners ◽  
Artefact Removal ◽  
Two Parameters

Artefacts removing (de-noising) from EEG signals has been an important aspect for medical practitioners for diagnosis of health issues related to brain. Several methods have been used in last few decades. Wavelet and total variation based de-noising have attracted the attention of engineers and scientists due to their de-noising efficiency. In this article, EEG signals have been de-noised using total variation based method and results obtained have been compared with the results obtained from the celebrated wavelet based methods . The performance of methods is measured using two parameters: signal-to-noise ratio and root mean square error. It has been observed that total variation based de-noising methods produce better results than the wavelet based methods.

Sign in / Sign up

Export Citation Format

Share Document