scholarly journals Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG

2022 ◽  
Vol 72 ◽  
pp. 103220
Author(s):  
Neng-Tai Chiu ◽  
Stephanie Huwiler ◽  
M. Laura Ferster ◽  
Walter Karlen ◽  
Hau-Tieng Wu ◽  
...  
Keyword(s):  
Single Channel ◽  
Artifact Detection

scholarly journals A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shalini Stalin ◽  
Vandana Roy ◽  
Prashant Kumar Shukla ◽  
Atef Zaguia ◽  
Mohammad Monirujjaman Khan ◽  
...  
Keyword(s):  
Single Channel ◽  
Motion Artifact ◽  
Ensemble Empirical Mode Decomposition ◽  
Motion Artifacts ◽  
Support Vector ◽  
Artifact Detection ◽  
Signal Features ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Neural Diseases

The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis (CCA) filtering approach is applied for motion artifact removal. Finally, leftover motion artifacts’ unpredictability is removed by applying wavelet transform (WT) algorithm. Finally, results are optimized by using Harris hawks optimization (HHO) algorithm. The results of the assessment confirm that the algorithm recommended is superior to the algorithms currently in use.

scholarly journals Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG

2021 ◽  
Author(s):  
Neng-Tai Chiu ◽  
Stephanie Huwiler ◽  
M. Laura Ferster ◽  
Walter Karlen ◽  
Hau-Tieng Wu ◽  
...  
Keyword(s):  
Single Channel ◽  
Linear Time ◽  
Brain Activity ◽  
Automated Detection ◽  
Artifact Detection ◽  
Time Frequency ◽  
Long Term Monitoring ◽  
Eeg Recordings ◽  
Term Monitoring

AbstractBrain activity recordings outside clinical or laboratory settings using mobile EEG systems have recently gained popular interest allowing for realistic long-term monitoring and eventually leading to identification of possible biomarkers for diseases. The less obtrusive, minimized systems (e.g. single-channel EEG, no ECG reference) have the drawback of artifact contamination with varying intensity that are particularly difficult to identify and remove. We developed brMEGA, the first algorithm for automated detection and removal of cardiogenic artifacts using non-linear time-frequency analysis and machine learning to (1) detect whether and where cardiogenic artifacts exist, and (2) remove those artifacts. We compare our algorithm against visual artifact identification and a previously established approach and validate it in one real and semi-real datasets. We demonstrated that brMEGA successfully identifies and substantially removes cardiogenic artifacts in single-channel EEG recordings. Moreover, recovery of cardiogenic artifacts gives the opportunity for future extraction of heart rate features without ECG measurement.

scholarly journals An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 602
Author(s):  
Elizaveta Saifutdinova ◽  
Marco Congedo ◽  
Daniela Dudysova ◽  
Lenka Lhotska ◽  
Jana Koprivova ◽  
...  
Keyword(s):  
Riemannian Geometry ◽  
Visual Inspection ◽  
Detection Method ◽  
Single Channel ◽  
Sleep Eeg ◽  
Biomedical Signal Processing ◽  
Biomedical Signal ◽  
Artifact Detection ◽  
Free Data ◽  
Eeg Artifacts

In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Many proposed methods concentrate on certain artifact types. Therefore, artifact-free data are often obtained after sequential application of different methods. Moreover, single-channel approaches must be applied to all channels alternately. The aim of this study is to develop a multichannel artifact detection method for multichannel sleep EEG capable of rejecting different artifact types at once. The inspiration for the study is gained from recent advances in the field of Riemannian geometry. The method we propose is tested on real datasets. The performance of the proposed method is measured by comparing detection results with the expert labeling as a reference and evaluated against a simpler method based on Riemannian geometry that has previously been proposed, as well as against the state-of-the-art method FASTER. The obtained results prove the effectiveness of the proposed method.

Data Acquisition and Handling Unit for a Quantitative Use of Electron Energy Loss Spectroscopy

1977 ◽  
Vol 35 ◽  
pp. 232-233 ◽  
Author(s):  
P. Trebbia ◽  
P. Ballongue ◽  
C. Colliex
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Electron Energy ◽  
Electron Energy Loss Spectroscopy ◽  
Single Channel ◽  
Detection System ◽  
Surface Barrier ◽  
Solid State Detector ◽  
Electrostatic Mirror ◽  
Electron Energy Loss

An effective use of electron energy loss spectroscopy for chemical characterization of selected areas in the electron microscope can only be achieved with the development of quantitative measurements capabilities.The experimental assembly, which is sketched in Fig.l, has therefore been carried out. It comprises four main elements.The analytical transmission electron microscope is a conventional microscope fitted with a Castaing and Henry dispersive unit (magnetic prism and electrostatic mirror). Recent modifications include the improvement of the vacuum in the specimen chamber (below 10-6 torr) and the adaptation of a new electrostatic mirror.The detection system, similar to the one described by Hermann et al (1), is located in a separate chamber below the fluorescent screen which visualizes the energy loss spectrum. Variable apertures select the electrons, which have lost an energy AE within an energy window smaller than 1 eV, in front of a surface barrier solid state detector RTC BPY 52 100 S.Q. The saw tooth signal delivered by a charge sensitive preamplifier (decay time of 5.10-5 S) is amplified, shaped into a gaussian profile through an active filter and counted by a single channel analyser.

Instrumentation for Measurement of Lingual Strength

1968 ◽  
Vol 11 (1) ◽  
pp. 189-193 ◽  
Author(s):  
Lois Joan Sanders
Keyword(s):  
Single Channel ◽  
Displacement Transducer ◽  
Tongue Pressure ◽  
Maximum Pressure ◽  
Research Use ◽  
Direct Writing ◽  
Significant Difference ◽  
Pressure Unit ◽  
Force Displacement ◽  
Consistent Response

A tongue pressure unit for measurement of lingual strength and patterns of tongue pressure is described. It consists of a force displacement transducer, a single channel, direct writing recording system, and a specially designed tongue pressure disk, head stabilizer, and pressure unit holder. Calibration with known weights indicated an essentially linear and consistent response. An evaluation of subject reliability in which 17 young adults were tested on two occasions revealed no significant difference in maximum pressure exerted during the two test trials. Suggestions for clinical and research use of the instrumentation are noted.

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