Literature DB >> 21436538

A comparison of three brain-computer interfaces based on event-related desynchronization, steady state visual evoked potentials, or a hybrid approach using both signals.

C Brunner1, B Z Allison, C Altstätter, C Neuper.   

Abstract

Brain-computer interface (BCI) systems rely on the direct measurement of brain signals, such as event-related desynchronization (ERD), steady state visual evoked potentials (SSVEPs), P300s, or slow cortical potentials. Unfortunately, none of these BCI approaches work for all users. This study compares two conventional BCI approaches (ERD and SSVEP) within subjects, and also evaluates a novel hybrid BCI based on a combination of these signals. We recorded EEG data from 12 subjects across three conditions. In the first condition, subjects imagined moving both hands or both feet (ERD). In the second condition, subjects focused on one of the two oscillating visual stimuli (SSVEP). In the third condition, subjects simultaneously performed both tasks. We used logarithmic band power features at sites and frequencies consistent with ERD and SSVEP activity, and subjects received real-time feedback based on their performance. Subjects also completed brief questionnaires. All subjects could simultaneously perform the movement and visual task in the hybrid condition even though most subjects had little or no training. All subjects showed both SSVEP and ERD activity during the hybrid task, consistent with the activity in both single tasks. Subjects generally considered the hybrid condition moderately more difficult, but all of them were able to complete the hybrid task. Results support the hypothesis that subjects who do not have strong ERD activity might be more effective with an SSVEP BCI, and suggest that SSVEP BCIs work for more subjects. A simultaneous hybrid BCI is feasible, although the current hybrid approach, which involves combining ERD and SSVEP in a two-choice task to improve accuracy, is not significantly better than a comparable SSVEP BCI. Switching to an SSVEP BCI could increase reliability in subjects who have trouble producing the EEG activity necessary to use an ERD BCI. Subjects who are proficient in both BCI approaches might be able to combine these approaches in different ways and for different goals.

Mesh:

Year:  2011        PMID: 21436538     DOI: 10.1088/1741-2560/8/2/025010

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


  13 in total

Review 1.  From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation.

Authors:  G Cheron; M Duvinage; C De Saedeleer; T Castermans; A Bengoetxea; M Petieau; K Seetharaman; T Hoellinger; B Dan; T Dutoit; F Sylos Labini; F Lacquaniti; Y Ivanenko
Journal:  Neural Plast       Date:  2012-01-04       Impact factor: 3.599

2.  On the quantification of SSVEP frequency responses in human EEG in realistic BCI conditions.

Authors:  Rafał Kuś; Anna Duszyk; Piotr Milanowski; Maciej Łabęcki; Maria Bierzyńska; Zofia Radzikowska; Magdalena Michalska; Jarosław Zygierewicz; Piotr Suffczyński; Piotr Jerzy Durka
Journal:  PLoS One       Date:  2013-10-18       Impact factor: 3.240

3.  Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface.

Authors:  Adrien Combaz; Marc M Van Hulle
Journal:  PLoS One       Date:  2015-03-27       Impact factor: 3.240

4.  Frequency- and Phase Encoded SSVEP Using Spatiotemporal Beamforming.

Authors:  Benjamin Wittevrongel; Marc M Van Hulle
Journal:  PLoS One       Date:  2016-08-03       Impact factor: 3.240

5.  Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP.

Authors:  Li-Wei Ko; S S K Ranga; Oleksii Komarov; Chung-Chiang Chen
Journal:  J Healthc Eng       Date:  2017-08-07       Impact factor: 2.682

Review 6.  A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives.

Authors:  Inchul Choi; Ilsun Rhiu; Yushin Lee; Myung Hwan Yun; Chang S Nam
Journal:  PLoS One       Date:  2017-04-28       Impact factor: 3.240

7.  Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing.

Authors:  Benjamin Wittevrongel; Marc M Van Hulle
Journal:  Front Neurosci       Date:  2017-11-15       Impact factor: 4.677

8.  Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.

Authors:  Jaeyoung Shin; Do-Won Kim; Klaus-Robert Müller; Han-Jeong Hwang
Journal:  Sensors (Basel)       Date:  2018-06-05       Impact factor: 3.576

9.  Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials.

Authors:  Tobias Kaufmann; Andreas Herweg; Andrea Kübler
Journal:  J Neuroeng Rehabil       Date:  2014-01-16       Impact factor: 4.262

10.  Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

Authors:  Benjamin Wittevrongel; Elia Van Wolputte; Marc M Van Hulle
Journal:  Sci Rep       Date:  2017-11-08       Impact factor: 4.379

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