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Curated Papers > High-impact authoritative literature on "brain-computer interfaces"

An auditory brain-computer interface (BCI). ​

Literature Information ​

DOI10.1016/j.jneumeth.2007.02.009
PMID17399797
JournalJournal of neuroscience methods
Impact Factor2.3
JCR QuartileQ3
Publication Year2008
Times Cited100
Keywordsauditory brain-computer interface, electroencephalography, sensorimotor rhythms, visual feedback, mood and motivation
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
ISSN0165-0270
Pages43-50
Issue167(1)
AuthorsFemke Nijboer, Adrian Furdea, Ingo Gunst, Jürgen Mellinger, Dennis J McFarland, Niels Birbaumer, Andrea Kübler

TL;DR ​

This study investigates the feasibility of an auditory brain-computer interface (BCI) for individuals with severely impaired vision, comparing it to a visual BCI. While initial performance was better in the visual feedback group, with adequate training, the auditory BCI showed comparable efficacy, highlighting the potential of auditory feedback in BCIs and the influence of mood and motivation on learning outcomes.

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auditory brain-computer interface · electroencephalography · sensorimotor rhythms · visual feedback · mood and motivation

Abstract ​

Brain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.

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Primary Questions Addressed ​

  1. What specific neurological disorders could benefit most from the implementation of auditory BCIs?
  2. How does the learning curve of auditory BCIs compare to that of traditional BCIs using visual feedback over a longer training period?
  3. In what ways could mood and motivation be effectively monitored and enhanced during BCI training sessions?
  4. What are the potential applications of auditory BCIs beyond communication for paralyzed patients?
  5. How might the findings of this study influence future research directions in brain-computer interface technology?

Key Findings ​

Research Background and Purpose ​

Brain-computer interfaces (BCIs) are systems that translate brain activity into commands for external devices, which are crucial for communication in severely paralyzed patients. Traditional BCIs often rely on visual stimuli, which can be problematic for patients with impaired vision. This study aims to investigate the feasibility of an auditory BCI using sensorimotor rhythms (SMR) as input, comparing its performance with visual feedback.

Main Methods/Materials/Experimental Design ​

The study involved 16 healthy volunteers divided into two groups: one receiving visual feedback and the other auditory feedback during three training sessions. Each session consisted of 30 runs where participants learned to modulate their SMR amplitude through motor imagery. Psychological factors, including mood and motivation, were assessed before each session.

Experimental Design Flowchart ​

Mermaid diagram

Key Results and Findings ​

  • Performance Comparison: The visual feedback group initially outperformed the auditory group (74.1% vs. 55.96% accuracy). However, by the end of the training, performance levels converged, with four participants from each group achieving over 70% accuracy.
  • Learning Curve: The auditory feedback group showed slower learning rates initially but improved significantly by the last session, indicating potential for effective communication with sufficient training.
  • Psychological Factors: Mood and mastery confidence positively correlated with performance in the visual feedback group, while fear of incompetence negatively impacted performance in both groups.

Main Conclusions/Significance/Innovation ​

The study concludes that auditory BCIs can be as effective as visual BCIs for communication with adequate training time. It highlights the importance of psychological factors in learning to use BCIs, suggesting that motivation and mood significantly influence performance. This research opens pathways for developing auditory BCIs for patients with visual impairments, emphasizing the need for further exploration in clinical populations.

Research Limitations and Future Directions ​

  • Limitations: The study was conducted on healthy volunteers, and the findings may not directly translate to patients with severe motor impairments. Additionally, the small sample size may limit generalizability.
  • Future Directions: Further research should focus on auditory BCI applications in clinical populations, particularly in patients with ALS or other conditions leading to locked-in states. Investigating the role of individual differences in sensory processing and psychological resilience could enhance BCI training protocols.

References ​

  1. Effects of acute tryptophan depletion on executive function in healthy male volunteers. - Peter Gallagher;Anna E Massey;Allan H Young;R Hamish McAllister-Williams - BMC psychiatry (2003)
  2. Brain-computer communication: unlocking the locked in. - A Kübler;B Kotchoubey;J Kaiser;J R Wolpaw;N Birbaumer - Psychological bulletin (2001)
  3. Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. - A Kübler;N Neumann;J Kaiser;B Kotchoubey;T Hinterberger;N P Birbaumer - Archives of physical medicine and rehabilitation (2001)
  4. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. - A Kübler;F Nijboer;J Mellinger;T M Vaughan;H Pawelzik;G Schalk;D J McFarland;N Birbaumer;J R Wolpaw - Neurology (2005)
  5. Effect of feedback signal and psychological characteristics on blood pressure self-manipulation capability. - S K Lal;R J Henderson;N Carter;A Bath;M G Hart;P Langeluddecke;S N Hunyor - Psychophysiology (1998)
  6. A spelling device for the paralysed. - N Birbaumer;N Ghanayim;T Hinterberger;I Iversen;B Kotchoubey;A Kübler;J Perelmouter;E Taub;H Flor - Nature (1999)
  7. A P300-based brain-computer interface: initial tests by ALS patients. - Eric W Sellers;Emanuel Donchin - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2006)
  8. A binary spelling interface with random errors. - J Perelmouter;N Birbaumer - IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2000)
  9. A multimodal brain-based feedback and communication system. - Thilo Hinterberger;Nicola Neumann;Mirko Pham;Andrea Kübler;Anke Grether;Nadine Hofmayer;Barbara Wilhelm;Herta Flor;Niels Birbaumer - Experimental brain research (2004)
  10. Amyotrophic lateral sclerosis. Communication status and survival with ventilatory support. - J R Bach - American journal of physical medicine & rehabilitation (1993)

Literatures Citing This Work ​

  1. Vibrotactile feedback for brain-computer interface operation. - Febo Cincotti;Laura Kauhanen;Fabio Aloise;Tapio Palomäki;Nicholas Caporusso;Pasi Jylänki;Donatella Mattia;Fabio Babiloni;Gerolf Vanacker;Marnix Nuttin;Maria Grazia Marciani;José Del R Millán - Computational intelligence and neuroscience (2007)
  2. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? - A Kübler;N Birbaumer - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2008)
  3. Toward a high-throughput auditory P300-based brain-computer interface. - D S Klobassa;T M Vaughan;P Brunner;N E Schwartz;J R Wolpaw;C Neuper;E W Sellers - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2009)
  4. Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. - Wei Wang;Jennifer L Collinger;Monica A Perez;Elizabeth C Tyler-Kabara;Leonardo G Cohen;Niels Birbaumer;Steven W Brose;Andrew B Schwartz;Michael L Boninger;Douglas J Weber - Physical medicine and rehabilitation clinics of North America (2010)
  5. A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. - Martijn Schreuder;Benjamin Blankertz;Michael Tangermann - PloS one (2010)
  6. (C)overt attention and visual speller design in an ERP-based brain-computer interface. - Matthias S Treder;Benjamin Blankertz - Behavioral and brain functions : BBF (2010)
  7. The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study. - Femke Nijboer;Niels Birbaumer;Andrea Kübler - Frontiers in neuroscience (2010)
  8. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. - J D R Millán;R Rupp;G R Müller-Putz;R Murray-Smith;C Giugliemma;M Tangermann;C Vidaurre;F Cincotti;A Kübler;R Leeb;C Neuper;K-R Müller;D Mattia - Frontiers in neuroscience (2010)
  9. Neuroengineering tools/applications for bidirectional interfaces, brain-computer interfaces, and neuroprosthetic implants - a review of recent progress. - Ryan Mark Rothschild - Frontiers in neuroengineering (2010)
  10. A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System. - Johannes Höhne;Martijn Schreuder;Benjamin Blankertz;Michael Tangermann - Frontiers in neuroscience (2011)

... (90 more literatures)

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