Cognitive Neurodynamics

, Volume 10, Issue 3, pp 201–209 | Cite as

An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps

Research Article

Abstract

Visual brain-computer interfaces (BCIs) are not suitable for people who cannot reliably maintain their eye gaze. Considering that this group usually maintains audition, an auditory based BCI may be a good choice for them. In this paper, we explore two auditory patterns: (1) a pattern utilizing symmetrical spatial cues with multiple frequency beeps [called the high low medium (HLM) pattern], and (2) a pattern utilizing non-symmetrical spatial cues with six tones derived from the diatonic scale [called the diatonic scale (DS) pattern]. These two patterns are compared to each other in terms of accuracy to determine which auditory pattern is better. The HLM pattern uses three different frequency beeps and has a symmetrical spatial distribution. The DS pattern uses six spoken stimuli, which are six notes solmizated as “do”, “re”, “mi”, “fa”, “sol” and “la”, and derived from the diatonic scale. These six sounds are distributed to six, spatially distributed, speakers. Thus, we compare a BCI paradigm using beeps with another BCI paradigm using tones on the diatonic scale, when the stimuli are spatially distributed. Although no significant differences are found between the ERPs, the HLM pattern performs better than the DS pattern: the online accuracy achieved with the HLM pattern is significantly higher than that achieved with the DS pattern (p = 0.0028).

Keywords

Auditory BCI Auditory pattern Spatial cues Diatonic scale 

Notes

Acknowledgments

This work was supported in part by the Grant National Natural Science Foundation of China, under Grant Numbers 61573142, 61203127, 91420302 and 61305028 and supported part by Shanghai Leading Academic Discipline Project, Project Number: B504. This work was also supported by the Fundamental Research Funds for the Central Universities (WG1414005, WH1314023, WH1516018).

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of EducationEast China University of Science and TechnologyShanghaiPeople’s Republic of China
  2. 2.Brain Embodiment Lab, School of Systems EngineeringUniversity of ReadingReadingUK
  3. 3.Lab for Advanced Brain Signal Processing and BTCCRiken BSIWako-shiJapan
  4. 4.Skolkowo Institute of Science and TechnologyMoscowRussia

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