Literature DB >> 25134085

FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing.

Ian Daly, Reinhold Scherer, Martin Billinger, Gernot Müller-Putz.   

Abstract

A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.

Entities:  

Mesh:

Year:  2014        PMID: 25134085     DOI: 10.1109/TNSRE.2014.2346621

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  20 in total

1.  Heading for new shores! Overcoming pitfalls in BCI design.

Authors:  Ricardo Chavarriaga; Melanie Fried-Oken; Sonja Kleih; Fabien Lotte; Reinhold Scherer
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2016-12-30

2.  Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music.

Authors:  Ian Daly; Duncan Williams; Faustina Hwang; Alexis Kirke; Eduardo R Miranda; Slawomir J Nasuto
Journal:  Sci Rep       Date:  2019-07-01       Impact factor: 4.379

3.  Detection of mental imagery and attempted movements in patients with disorders of consciousness using EEG.

Authors:  Petar Horki; Günther Bauernfeind; Daniela S Klobassa; Christoph Pokorny; Gerald Pichler; Walter Schippinger; Gernot R Müller-Putz
Journal:  Front Hum Neurosci       Date:  2014-12-12       Impact factor: 3.169

4.  Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography.

Authors:  Maitreyee Wairagkar; Yoshikatsu Hayashi; Slawomir J Nasuto
Journal:  PLoS One       Date:  2018-03-06       Impact factor: 3.240

Review 5.  EEG-Informed fMRI: A Review of Data Analysis Methods.

Authors:  Rodolfo Abreu; Alberto Leal; Patrícia Figueiredo
Journal:  Front Hum Neurosci       Date:  2018-02-06       Impact factor: 3.169

Review 6.  Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

Authors:  Keum-Shik Hong; Muhammad Jawad Khan
Journal:  Front Neurorobot       Date:  2017-07-24       Impact factor: 2.650

7.  A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings.

Authors:  Gabriella Tamburro; Patrique Fiedler; David Stone; Jens Haueisen; Silvia Comani
Journal:  PeerJ       Date:  2018-02-23       Impact factor: 2.984

8.  Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task.

Authors:  Andreas Meinel; Sebastián Castaño-Candamil; Janine Reis; Michael Tangermann
Journal:  Front Hum Neurosci       Date:  2016-04-25       Impact factor: 3.169

9.  Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application.

Authors:  Angel Mur; Raquel Dormido; Jesús Vega; Natividad Duro; Sebastian Dormido-Canto
Journal:  Sensors (Basel)       Date:  2016-04-23       Impact factor: 3.576

10.  The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users.

Authors:  Serafeim Perdikis; Luca Tonin; Sareh Saeedi; Christoph Schneider; José Del R Millán
Journal:  PLoS Biol       Date:  2018-05-10       Impact factor: 8.029

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