Literature DB >> 21571004

Should the parameters of a BCI translation algorithm be continually adapted?

Dennis J McFarland1, William A Sarnacki, Jonathan R Wolpaw.   

Abstract

People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21571004      PMCID: PMC3134307          DOI: 10.1016/j.jneumeth.2011.04.037

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  24 in total

1.  A fully on-line adaptive BCI.

Authors:  C Vidaurre; A Schlögl; A Schlöogl; R Cabeza; R Scherer; G Pfurtscheller
Journal:  IEEE Trans Biomed Eng       Date:  2006-06       Impact factor: 4.538

2.  BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.

Authors:  Dennis J McFarland; Charles W Anderson; Klaus-Robert Müller; Alois Schlögl; Dean J Krusienski
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2006-06       Impact factor: 3.802

3.  The BCI competition. III: Validating alternative approaches to actual BCI problems.

Authors:  Benjamin Blankertz; Klaus-Robert Müller; Dean J Krusienski; Gerwin Schalk; Jonathan R Wolpaw; Alois Schlögl; Gert Pfurtscheller; José del R Millán; Michael Schröder; Niels Birbaumer
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2006-06       Impact factor: 3.802

4.  Robust classification of EEG signal for brain-computer interface.

Authors:  Manoj Thulasidas; Cuntai Guan; Jiankang Wu
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2006-03       Impact factor: 3.802

5.  A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.

Authors:  Eric W Sellers; Dean J Krusienski; Dennis J McFarland; Theresa M Vaughan; Jonathan R Wolpaw
Journal:  Biol Psychol       Date:  2006-07-24       Impact factor: 3.251

6.  Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tilings.

Authors:  Nuri Firat Ince; Sami Arica; Ahmed Tewfik
Journal:  J Neural Eng       Date:  2006-07-20       Impact factor: 5.379

7.  Toward enhanced P300 speller performance.

Authors:  D J Krusienski; E W Sellers; D J McFarland; T M Vaughan; J R Wolpaw
Journal:  J Neurosci Methods       Date:  2007-08-01       Impact factor: 2.390

8.  Towards adaptive classification for BCI.

Authors:  Pradeep Shenoy; Matthias Krauledat; Benjamin Blankertz; Rajesh P N Rao; Klaus-Robert Müller
Journal:  J Neural Eng       Date:  2006-03-01       Impact factor: 5.379

9.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.

Authors:  Jonathan R Wolpaw; Dennis J McFarland
Journal:  Proc Natl Acad Sci U S A       Date:  2004-12-07       Impact factor: 11.205

Review 10.  Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.

Authors:  Dennis J McFarland; Dean J Krusienski; Jonathan R Wolpaw
Journal:  Prog Brain Res       Date:  2006       Impact factor: 2.453

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  8 in total

1.  Neural decoding of attentional selection in multi-speaker environments without access to clean sources.

Authors:  James O'Sullivan; Zhuo Chen; Jose Herrero; Guy M McKhann; Sameer A Sheth; Ashesh D Mehta; Nima Mesgarani
Journal:  J Neural Eng       Date:  2017-08-04       Impact factor: 5.379

2.  ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance.

Authors:  Surjo R Soekadar; Matthias Witkowski; Jürgen Mellinger; Ander Ramos; Niels Birbaumer; Leonardo G Cohen
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-10       Impact factor: 3.802

Review 3.  Brain-computer interfaces for amyotrophic lateral sclerosis.

Authors:  Dennis J McFarland
Journal:  Muscle Nerve       Date:  2020-06       Impact factor: 3.217

4.  EEG-Based Brain-Computer Interfaces.

Authors:  D J McFarland; J R Wolpaw
Journal:  Curr Opin Biomed Eng       Date:  2017-11-28

5.  The advantages of the surface Laplacian in brain-computer interface research.

Authors:  Dennis J McFarland
Journal:  Int J Psychophysiol       Date:  2014-08-01       Impact factor: 2.997

6.  Operationalizing Cognitive Science and Technologies' Research and Development; the "Brain and Cognition Study Group (BCSG)" Initiative from Shiraz, Iran.

Authors:  Nahid Ashjazadeh; Reza Boostani; Hamed Ekhtiari; Masoumeh Emamghoreishi; Majidreza Farrokhi; Ahmad Ghanizadeh; Gholamreza Hatam; Habib Hadianfard; Mehrzad Lotfi; Seyed Mohammad Javad Mortazavi; Maryam Mousavi; Afshin Montakhab; Majid Nili; Ali Razmkon; Sina Salehi; Amir Mohammad Sodagar; Peiman Setoodeh; Mousa Taghipour; Mohammad Torabi-Nami; Abdolkarim Vesal
Journal:  Basic Clin Neurosci       Date:  2014

7.  Towards reconstructing intelligible speech from the human auditory cortex.

Authors:  Hassan Akbari; Bahar Khalighinejad; Jose L Herrero; Ashesh D Mehta; Nima Mesgarani
Journal:  Sci Rep       Date:  2019-01-29       Impact factor: 4.379

8.  Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution.

Authors:  Johannes Höhne; Elisa Holz; Pit Staiger-Sälzer; Klaus-Robert Müller; Andrea Kübler; Michael Tangermann
Journal:  PLoS One       Date:  2014-08-27       Impact factor: 3.240

  8 in total

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