Filters








7,862 Hits in 5.7 sec

Real-time EEG artifact correction during fMRI using ICA

Ahmad Mayeli, Vadim Zotev, Hazem Refai, Jerzy Bodurka
2016 Journal of Neuroscience Methods  
New methods: We introduce a novel, improved approach for real-time EEG artifact correction during fMRI (rtICA).  ...  h i g h l i g h t s • A real-time method based on ICA (rtICA) is proposed to remove artifacts from EEG data acquired simultaneously with fMRI. • The rtICA effectively reduces ocular, motion, BCG, muscle  ...  We propose a novel algorithm based on ICA for attenuating all types of artifact in EEG data acquired during fMRI scans.  ... 
doi:10.1016/j.jneumeth.2016.09.012 pmid:27697458 fatcat:g52v6mxi7vavhasnytryi3jkqe

Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI

Kai Wang, Wenjie Li, Li Dong, Ling Zou, Changming Wang
2018 Frontiers in Neuroscience  
In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts.  ...  In conclusion, our novel method successfully and effectively removed BCG artifacts in both simulated and vivo EEG data tests, showing the potentials of removing artifacts in EEG-fMRI applications.  ...  The results for both time and frequency domains showed that our method is promising in removing BCG artifacts from EEG data recorded in MRI environment.  ... 
doi:10.3389/fnins.2018.00059 pmid:29487499 pmcid:PMC5816921 fatcat:qcv2dwu4obdwjmde2y7fzvipne

A Novel Method Based on Combination of Independent Component Analysis and Ensemble Empirical Mode Decomposition for Removing Electrooculogram Artifacts From Multichannel Electroencephalogram Signals

Chao-Lin Teng, Yi-Yang Zhang, Wei Wang, Yuan-Yuan Luo, Gang Wang, Jin Xu
2021 Frontiers in Neuroscience  
In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals  ...  This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.  ...  In this study, by combining the EEMD and ICA algorithms, we proposed a novel EEMD-based ICA (EICA) method to remove blink artifacts from multichannel contaminated EEG signals.  ... 
doi:10.3389/fnins.2021.729403 pmid:34707475 pmcid:PMC8542780 fatcat:kijyqerhyndw3l3hdgqzkltzb4

Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal

Vandana Roy, Shailja Shukla, Piyush Kumar Shukla, Paresh Rawat
2017 Journal of Healthcare Engineering  
This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data.  ...  The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.  ...  Thus, a novel algorithm GECCA is introduced in cascade with EEMD and SWT for fast and effective suppression of motion artifacts from single-channel EEG signal.  ... 
doi:10.1155/2017/9674712 pmid:29118966 pmcid:PMC5651166 fatcat:5kx7wjjojjfqloas34jutkgqza

Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data

Heike Leutheuser, Florian Gabsteiger, Felix Hebenstreit, Pedro Reis, Matthias Lochmann, Bjoern Eskofier
2013 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm InfoMax and the AMICA algorithm.  ...  A novel objective measure enabled to compare both algorithms according to their performance. Results showed that the AMICA algorithm outperformed the InfoMax algorithm.  ...  ACKNOWLEDGMENT This work was supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology and the European fund for regional development.  ... 
doi:10.1109/embc.2013.6611119 pmid:24111306 dblp:conf/embc/LeutheuserGHRLE13 fatcat:fxjax7lkgre3pkaucorqtppfyu

Automatic Removal of Ocular Artifact from EEG with DWT and ICA Method

Mingai Li, Yan Cui, Jinfu Yang
2013 Applied Mathematics & Information Sciences  
To overcome OA interference in EEG data, a novel automatic method of OA removal, denoted as DWICA, was proposed in this paper.  ...  Ocular artifact (OA) is one of the main interferences in electroencephalogram (EEG) recordings. It appears as a big pulse and has a strong impact to EEG signals.  ...  The authors are grateful to the anonymous referee for a careful checking of the details and for helpful comments that improved this paper.  ... 
doi:10.12785/amis/070252 fatcat:igrgh5m66zasjep7b34eyyy2su

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

Kevin T. Sweeney, Seán F. McLoone, Tomás E. Ward
2013 IEEE Transactions on Biomedical Engineering  
This paper describes a novel artifact removal technique for use in such a context.  ...  If multichannel recordings are available for a given signal source then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts  ...  [2] and for ocular artifact removal from EEG by Kumar et al . [7] . B.  ... 
doi:10.1109/tbme.2012.2225427 pmid:23086501 fatcat:xfs5hx7bufdcrl4u6y6ekceqrq

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Puneet Mishra, Sunil Kumar Singla
2013 Measurement Science Review  
This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA).  ...  Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.  ...  The approach mentioned here will rectify the traditional method of artifact removal while evaluating EEG or ECG for biometric authentication.  ... 
doi:10.2478/msr-2013-0001 fatcat:v2q66wvm3je2boqluxvhddoaim

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

Md Shafayet Hossain, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, Ahmad Ashrif A. Bakar, Serkan Kiranyaz, Amith Khandakar, Mohammed Alhatou, Rumana Habib, Muhammad Maqsud Hossain
2022 Sensors  
Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts  ...  from single-channel EEG and fNIRS signals.  ...  Motion Artifact Correction from EEG Data All the algorithms (18 in total) were applied on all the 23 recordings of EEG.  ... 
doi:10.3390/s22093169 pmid:35590859 pmcid:PMC9102309 fatcat:pbiex2yzvfbaxgb3atlricnsf4

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis [article]

Md Shafayet Hossain, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal H. M. Ali, Ahmad Ashrif A. Bakar, Serkan Kiranyaz, Amith Khandakar, Mohammed Alhatou, Rumana Habib, Muhammad Maqsud Hossain
2022 arXiv   pre-print
dB and 59.51 respectively for all the EEG recordings.  ...  using db1 wavelet packet for all the available 23 EEG recordings.  ...  Motion Artifact Correction from EEG data: All the algorithms (18 in total) were applied on all the 23 recordings of EEG.  ... 
arXiv:2204.04533v1 fatcat:hxao26ixxfflldyc4aralr6onq

Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

Malik Mannan, Shinjung Kim, Myung Jeong, M. Kamran
2016 Sensors  
from EEG data.  ...  Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI).  ...  Conclusions This paper presents a novel algorithm using hybrid EEG and eye tracker system to automatically identify and remove ocular artifacts from EEG data by combining ICA and auto-regressive exogenous  ... 
doi:10.3390/s16020241 pmid:26907276 pmcid:PMC4801617 fatcat:ej7zmr5lvjh4rngalapokrquey

AR2, a novel automatic artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

Shennan Aibel Weiss, Ali A Asadi-Pooya, Sitaram Vangala, Stephanie Moy, Dale H Wyeth, Iren Orosz, Michael Gibbs, Lara Schrader, Jason Lerner, Christopher K Cheng, Edward Chang, Rajsekar Rajaraman (+20 others)
2017 F1000Research  
Vergult A, De Clercq W, Palmini A, et al.: Improving the interpretation of ictal scalp EEG: BSS-CCA algorithm for muscle artifact removal. Epilepsia. 2007; 48(5): 950-8.  ...  AR2, a novel automatic artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software [version 1; referees: 2 approved 2017  ...  Click here to access the data.  ... 
doi:10.12688/f1000research.10569.1 pmid:28491280 pmcid:PMC5399961 fatcat:prvaugwvgfgpfahdqb3l7guqfe

Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage

Madeleine Bullock, Graeme D. Jackson, David F. Abbott
2021 Frontiers in Neurology  
This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published  ...  Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics.  ...  Finally, Abolghasemi and Ferdowsi (116) used dictionary learning for removal of BCG from EEG data, where a sparse dictionary is able to model the BCG data from EEG-fMRI data.  ... 
doi:10.3389/fneur.2021.622719 pmid:33776886 pmcid:PMC7991907 fatcat:mwrddhfc2ndvjl6tho32fxxkf4

Removal of EMG Artifacts from Multichannel EEG Signal Using Automatic Dynamic Segmentation and Adaptive Thresholding with Multilevel Decomposed Wavelets

K.P. Paradeshi, Research Scholar, Professor Dr. U.D. Kolekar
2017 IOSR Journal of Electrical and Electronics Engineering  
A particular segment can be analyzed and processed independent of other segments. The present adaptive threshold method is best suitable for removal of muscle artifacts.  ...  The present work deals with novel automatic dynamic size independent components based on statistical information of signal and development of multilevel decomposition with adaptive threshold for removing  ...  A novel technique (Automatic Wavelet Independent Component Analysis, AWICA) for automatic EEG artifact removal is presented.  ... 
doi:10.9790/1676-1203043035 fatcat:qbwttp6b65b65bthto4me5hw3i

AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

Shennan Aibel Weiss, Ali A Asadi-Pooya, Sitaram Vangala, Stephanie Moy, Dale H Wyeth, Iren Orosz, Michael Gibbs, Lara Schrader, Jason Lerner, Christopher K Cheng, Edward Chang, Rajsekar Rajaraman (+20 others)
2017 F1000Research  
However, the assignments by groups of readers are often congruent with other clinical data. Utilization of the AR2 software method may improve the validity of ictal EEG artifact reduction.  ...  EEG artifact reduction methods for localizing seizure-onset does Conclusions: not result in high rates of interpretability, reader confidence, and inter-reader agreement.  ...  Sandra Dewar for her administrative assistance, and Mr. Kirk Shattuck for his technical support.  ... 
doi:10.12688/f1000research.10569.2 pmid:28491280 pmcid:PMC5399961 fatcat:6zpywss2zzhyxl3mcwqw4c7eiq
« Previous Showing results 1 — 15 out of 7,862 results