Literature DB >> 20332550

Toward a hybrid brain-computer interface based on imagined movement and visual attention.

B Z Allison1, C Brunner, V Kaiser, G R Müller-Putz, C Neuper, G Pfurtscheller.   

Abstract

Brain-computer interface (BCI) systems do not work for all users. This article introduces a novel combination of tasks that could inspire BCI systems that are more accurate than conventional BCIs, especially for users who cannot attain accuracy adequate for effective communication. Subjects performed tasks typically used in two BCI approaches, namely event-related desynchronization (ERD) and steady state visual evoked potential (SSVEP), both individually and in a 'hybrid' condition that combines both tasks. Electroencephalographic (EEG) data were recorded across three conditions. Subjects imagined moving the left or right hand (ERD), focused on one of the two oscillating visual stimuli (SSVEP), and then simultaneously performed both tasks. Accuracy and subjective measures were assessed. Offline analyses suggested that half of the subjects did not produce brain patterns that could be accurately discriminated in response to at least one of the two tasks. If these subjects produced comparable EEG patterns when trying to use a BCI, these subjects would not be able to communicate effectively because the BCI would make too many errors. Results also showed that switching to a different task used in BCIs could improve accuracy in some of these users. Switching to a hybrid approach eliminated this problem completely, and subjects generally did not consider the hybrid condition more difficult. Results validate this hybrid approach and suggest that subjects who cannot use a BCI should consider switching to a different BCI approach, especially a hybrid BCI. Subjects proficient with both approaches might combine them to increase information throughput by improving accuracy, reducing selection time, and/or increasing the number of possible commands.

Entities:  

Mesh:

Year:  2010        PMID: 20332550     DOI: 10.1088/1741-2560/7/2/026007

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  47 in total

1.  Investigation of the effect of EEG-BCI on the simultaneous execution of flight simulation and attentional tasks.

Authors:  Giovanni Vecchiato; Gianluca Borghini; Pietro Aricò; Ilenia Graziani; Anton Giulio Maglione; Patrizia Cherubino; Fabio Babiloni
Journal:  Med Biol Eng Comput       Date:  2015-12-08       Impact factor: 2.602

2.  Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

Authors:  David P McMullen; Guy Hotson; Kapil D Katyal; Brock A Wester; Matthew S Fifer; Timothy G McGee; Andrew Harris; Matthew S Johannes; R Jacob Vogelstein; Alan D Ravitz; William S Anderson; Nitish V Thakor; Nathan E Crone
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-12-12       Impact factor: 3.802

3.  Early detection of hand movements from electroencephalograms for stroke therapy applications.

Authors:  A Muralidharan; J Chae; D M Taylor
Journal:  J Neural Eng       Date:  2011-05-27       Impact factor: 5.379

4.  Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb.

Authors:  Petar Horki; Teodoro Solis-Escalante; Christa Neuper; Gernot Müller-Putz
Journal:  Med Biol Eng Comput       Date:  2011-03-11       Impact factor: 2.602

5.  EEG-based hybrid QWERTY mental speller with high information transfer rate.

Authors:  Er Akshay Katyal; Rajesh Singla
Journal:  Med Biol Eng Comput       Date:  2021-02-16       Impact factor: 2.602

6.  Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.

Authors:  Jennifer Stiso; Marie-Constance Corsi; Jean M Vettel; Javier Garcia; Fabio Pasqualetti; Fabrizio De Vico Fallani; Timothy H Lucas; Danielle S Bassett
Journal:  J Neural Eng       Date:  2020-07-24       Impact factor: 5.379

7.  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

8.  An asynchronous wheelchair control by hybrid EEG-EOG brain-computer interface.

Authors:  Hongtao Wang; Yuanqing Li; Jinyi Long; Tianyou Yu; Zhenghui Gu
Journal:  Cogn Neurodyn       Date:  2014-05-24       Impact factor: 5.082

9.  Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.

Authors:  Bradley J Edelman; Jianjun Meng; Nicholas Gulachek; Christopher C Cline; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-05       Impact factor: 3.802

10.  The hybrid BCI.

Authors:  Gert Pfurtscheller; Brendan Z Allison; Clemens Brunner; Gunther Bauernfeind; Teodoro Solis-Escalante; Reinhold Scherer; Thorsten O Zander; Gernot Mueller-Putz; Christa Neuper; Niels Birbaumer
Journal:  Front Neurosci       Date:  2010-04-21       Impact factor: 4.677

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.