Literature DB >> 19174332

xDAWN algorithm to enhance evoked potentials: application to brain-computer interface.

Bertrand Rivet1, Antoine Souloumiac, Virginie Attina, Guillaume Gibert.   

Abstract

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier , show that the proposed method is efficient and accurate.

Mesh:

Year:  2009        PMID: 19174332     DOI: 10.1109/TBME.2009.2012869

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  51 in total

Review 1.  Brain computer interfaces, a review.

Authors:  Luis Fernando Nicolas-Alonso; Jaime Gomez-Gil
Journal:  Sensors (Basel)       Date:  2012-01-31       Impact factor: 3.576

2.  A P300-based brain-computer interface aimed at operating electronic devices at home for severely disabled people.

Authors:  Rebeca Corralejo; Luis F Nicolás-Alonso; Daniel Alvarez; Roberto Hornero
Journal:  Med Biol Eng Comput       Date:  2014-08-28       Impact factor: 2.602

Review 3.  Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI.

Authors:  S Chevallier; E K Kalunga; Q Barthélemy; E Monacelli
Journal:  Neuroinformatics       Date:  2021-01

4.  Channel selection methods for the P300 Speller.

Authors:  K A Colwell; D B Ryan; C S Throckmorton; E W Sellers; L M Collins
Journal:  J Neurosci Methods       Date:  2014-05-02       Impact factor: 2.390

5.  Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans.

Authors:  Cédric Simar; Robin Petit; Nichita Bozga; Axelle Leroy; Ana-Maria Cebolla; Mathieu Petieau; Gianluca Bontempi; Guy Cheron
Journal:  PLoS One       Date:  2022-01-14       Impact factor: 3.240

6.  Comparison of sensor selection mechanisms for an ERP-based brain-computer interface.

Authors:  David Feess; Mario M Krell; Jan H Metzen
Journal:  PLoS One       Date:  2013-07-02       Impact factor: 3.240

7.  Changes in event-related potential functional networks predict traumatic brain injury in piglets.

Authors:  Lorre S Atlan; Ingrid S Lan; Colin Smith; Susan S Margulies
Journal:  Clin Biomech (Bristol, Avon)       Date:  2018-06-01       Impact factor: 2.063

8.  A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision.

Authors:  Davide Borra; Silvia Fantozzi; Elisa Magosso
Journal:  Front Hum Neurosci       Date:  2021-07-08       Impact factor: 3.169

9.  Estimating the intended sound direction of the user: toward an auditory brain-computer interface using out-of-head sound localization.

Authors:  Isao Nambu; Masashi Ebisawa; Masumi Kogure; Shohei Yano; Haruhide Hokari; Yasuhiro Wada
Journal:  PLoS One       Date:  2013-02-20       Impact factor: 3.240

10.  Channel selection based on phase measurement in P300-based brain-computer interface.

Authors:  Minpeng Xu; Hongzhi Qi; Lan Ma; Changcheng Sun; Lixin Zhang; Baikun Wan; Tao Yin; Dong Ming
Journal:  PLoS One       Date:  2013-04-11       Impact factor: 3.240

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