Literature DB >> 22498703

Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control.

Dandan Huang1, Kai Qian, Ding-Yu Fei, Wenchuan Jia, Xuedong Chen, Ou Bai.   

Abstract

This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemoral feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users.

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Year:  2012        PMID: 22498703     DOI: 10.1109/TNSRE.2012.2190299

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


  26 in total

1.  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

2.  Bayesian spatial filters for source signal extraction: a study in the peripheral nerve.

Authors:  Y Tang; B Wodlinger; D M Durand
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-03       Impact factor: 3.802

3.  Exploring EEG spectral and temporal dynamics underlying a hand grasp movement.

Authors:  Sandeep Bodda; Shyam Diwakar
Journal:  PLoS One       Date:  2022-06-23       Impact factor: 3.752

4.  Benefits of deep learning classification of continuous noninvasive brain-computer interface control.

Authors:  James R Stieger; Stephen A Engel; Daniel Suma; Bin He
Journal:  J Neural Eng       Date:  2021-06-09       Impact factor: 5.043

5.  Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot.

Authors:  Giuseppe Lisi; Tomoyuki Noda; Jun Morimoto
Journal:  Front Syst Neurosci       Date:  2014-05-14

6.  A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.

Authors:  Álvaro Costa; Enrique Hortal; Eduardo Iáñez; José M Azorín
Journal:  PLoS One       Date:  2014-11-12       Impact factor: 3.240

Review 7.  Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.

Authors:  Daniela Camargo-Vargas; Mauro Callejas-Cuervo; Stefano Mazzoleni
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

8.  EEG resolutions in detecting and decoding finger movements from spectral analysis.

Authors:  Ran Xiao; Lei Ding
Journal:  Front Neurosci       Date:  2015-09-01       Impact factor: 4.677

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.  Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface.

Authors:  M Jawad Khan; Melissa Jiyoun Hong; Keum-Shik Hong
Journal:  Front Hum Neurosci       Date:  2014-04-28       Impact factor: 3.169

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