Literature DB >> 24691154

Is sensorimotor BCI performance influenced differently by mono, stereo, or 3-D auditory feedback?

Karl A McCreadie, Damien H Coyle, Girijesh Prasad.   

Abstract

Imagination of movement can be used as a control method for a brain-computer interface (BCI) allowing communication for the physically impaired. Visual feedback within such a closed loop system excludes those with visual problems and hence there is a need for alternative sensory feedback pathways. In the context of substituting the visual channel for the auditory channel, this study aims to add to the limited evidence that it is possible to substitute visual feedback for its auditory equivalent and assess the impact this has on BCI performance. Secondly, the study aims to determine for the first time if the type of auditory feedback method influences motor imagery performance significantly. Auditory feedback is presented using a stepped approach of single (mono), double (stereo), and multiple (vector base amplitude panning as an audio game) loudspeaker arrangements. Visual feedback involves a ball-basket paradigm and a spaceship game. Each session consists of either auditory or visual feedback only with runs of each type of feedback presentation method applied in each session. Results from seven subjects across five sessions of each feedback type (visual, auditory) (10 sessions in total) show that auditory feedback is a suitable substitute for the visual equivalent and that there are no statistical differences in the type of auditory feedback presented across five sessions.

Mesh:

Year:  2014        PMID: 24691154     DOI: 10.1109/TNSRE.2014.2312270

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


  9 in total

1.  Designing Guiding Systems for Brain-Computer Interfaces.

Authors:  Nataliya Kosmyna; Anatole Lécuyer
Journal:  Front Hum Neurosci       Date:  2017-07-31       Impact factor: 3.169

2.  User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

Authors:  Minkyu Ahn; Hohyun Cho; Sangtae Ahn; Sung C Jun
Journal:  Front Hum Neurosci       Date:  2018-02-15       Impact factor: 3.169

3.  Acoustic Neurofeedback Increases Beta ERD During Mental Rotation Task.

Authors:  Wioletta Karina Ozga; Dariusz Zapała; Piotr Wierzgała; Paweł Augustynowicz; Robert Porzak; Grzegorz Marcin Wójcik
Journal:  Appl Psychophysiol Biofeedback       Date:  2019-06

4.  Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study.

Authors:  Xin Li; Lu Wang; Si Miao; Zan Yue; Zhiming Tang; Liujie Su; Yadan Zheng; Xiangzhen Wu; Shan Wang; Jing Wang; Zulin Dou
Journal:  Front Neurosci       Date:  2022-03-11       Impact factor: 4.677

5.  Enhancement of lower limb motor imagery ability via dual-level multimodal stimulation and sparse spatial pattern decoding method.

Authors:  Yao Hou; Zhenghui Gu; Zhu Liang Yu; Xiaofeng Xie; Rongnian Tang; Jinghan Xu; Feifei Qi
Journal:  Front Hum Neurosci       Date:  2022-08-11       Impact factor: 3.473

6.  Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia.

Authors:  Attila Korik; Karl McCreadie; Niall McShane; Naomi Du Bois; Massoud Khodadadzadeh; Jacqui Stow; Jacinta McElligott; Áine Carroll; Damien Coyle
Journal:  J Neuroeng Rehabil       Date:  2022-09-06       Impact factor: 5.208

7.  Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system.

Authors:  Yuanqing Li; Jiahui Pan; Yanbin He; Fei Wang; Steven Laureys; Qiuyou Xie; Ronghao Yu
Journal:  BMC Neurol       Date:  2015-12-15       Impact factor: 2.474

Review 8.  A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems.

Authors:  James Wright; Vaughan G Macefield; André van Schaik; Jonathan C Tapson
Journal:  Front Neurosci       Date:  2016-07-12       Impact factor: 4.677

Review 9.  A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface.

Authors:  Amardeep Singh; Ali Abdul Hussain; Sunil Lal; Hans W Guesgen
Journal:  Sensors (Basel)       Date:  2021-03-20       Impact factor: 3.576

  9 in total

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