Literature DB >> 22626956

An auditory brain–computer interface evoked by natural speech.

M A Lopez-Gordo1, E Fernandez, S Romero, F Pelayo, Alberto Prieto.   

Abstract

Brain–computer interfaces (BCIs) are mainly intended for people unable to perform any muscular movement, such as patients in a complete locked-in state. The majority of BCIs interact visually with the user, either in the form of stimulation or biofeedback. However, visual BCIs challenge their ultimate use because they require the subjects to gaze, explore and shift eye-gaze using their muscles, thus excluding patients in a complete locked-in state or under the condition of the unresponsive wakefulness syndrome. In this study, we present a novel fully auditory EEG-BCI based on a dichotic listening paradigm using human voice for stimulation. This interface has been evaluated with healthy volunteers, achieving an average information transmission rate of 1.5 bits min⁻¹ in full-length trials and 2.7 bits min⁻¹ using the optimal length of trials, recorded with only one channel and without formal training. This novel technique opens the door to a more natural communication with users unable to use visual BCIs, with promising results in terms of performance, usability, training and cognitive effort.

Entities:  

Mesh:

Year:  2012        PMID: 22626956     DOI: 10.1088/1741-2560/9/3/036013

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


  13 in total

1.  Usage of drip drops as stimuli in an auditory P300 BCI paradigm.

Authors:  Minqiang Huang; Jing Jin; Yu Zhang; Dewen Hu; Xingyu Wang
Journal:  Cogn Neurodyn       Date:  2017-11-16       Impact factor: 5.082

2.  Selective attention in an overcrowded auditory scene: implications for auditory-based brain-computer interface design.

Authors:  Ross K Maddox; Willy Cheung; Adrian K C Lee
Journal:  J Acoust Soc Am       Date:  2012-11       Impact factor: 1.840

3.  Effects of augmentative visual training on audio-motor mapping.

Authors:  Gabrielle L Hands; Eric Larson; Cara E Stepp
Journal:  Hum Mov Sci       Date:  2014-02-12       Impact factor: 2.161

4.  Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals.

Authors:  K Dijkstra; P Brunner; A Gunduz; W Coon; A L Ritaccio; J Farquhar; G Schalk
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2015-08-26

5.  Combined Auditory and Vibrotactile Feedback for Human-Machine-Interface Control.

Authors:  Elias B Thorp; Eric Larson; Cara E Stepp
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-07-31       Impact factor: 3.802

6.  Communication and control by listening: toward optimal design of a two-class auditory streaming brain-computer interface.

Authors:  N Jeremy Hill; Aisha Moinuddin; Ann-Katrin Häuser; Stephan Kienzle; Gerwin Schalk
Journal:  Front Neurosci       Date:  2012-12-19       Impact factor: 4.677

7.  An auditory multiclass brain-computer interface with natural stimuli: Usability evaluation with healthy participants and a motor impaired end user.

Authors:  Nadine Simon; Ivo Käthner; Carolin A Ruf; Emanuele Pasqualotto; Andrea Kübler; Sebastian Halder
Journal:  Front Hum Neurosci       Date:  2015-01-09       Impact factor: 3.169

8.  Categorical vowel perception enhances the effectiveness and generalization of auditory feedback in human-machine-interfaces.

Authors:  Eric Larson; Howard P Terry; Margaux M Canevari; Cara E Stepp
Journal:  PLoS One       Date:  2013-03-19       Impact factor: 3.240

9.  Quantifying attentional modulation of auditory-evoked cortical responses from single-trial electroencephalography.

Authors:  Inyong Choi; Siddharth Rajaram; Lenny A Varghese; Barbara G Shinn-Cunningham
Journal:  Front Hum Neurosci       Date:  2013-04-04       Impact factor: 3.169

10.  Towards user-friendly spelling with an auditory brain-computer interface: the CharStreamer paradigm.

Authors:  Johannes Höhne; Michael Tangermann
Journal:  PLoS One       Date:  2014-06-02       Impact factor: 3.240

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