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

Research Article
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Abstract

Recently, many auditory BCIs are using beeps as auditory stimuli, while beeps sound unnatural and unpleasant for some people. It is proved that natural sounds make people feel comfortable, decrease fatigue, and improve the performance of auditory BCI systems. Drip drop is a kind of natural sounds that makes humans feel relaxed and comfortable. In this work, three kinds of drip drops were used as stimuli in an auditory-based BCI system to improve the user-friendness of the system. This study explored whether drip drops could be used as stimuli in the auditory BCI system. The auditory BCI paradigm with drip-drop stimuli, which was called the drip-drop paradigm (DP), was compared with the auditory paradigm with beep stimuli, also known as the beep paradigm (BP), in items of event-related potential amplitudes, online accuracies and scores on the likability and difficulty to demonstrate the advantages of DP. DP obtained significantly higher online accuracy and information transfer rate than the BP (p < 0.05, Wilcoxon signed test; p < 0.05, Wilcoxon signed test). Besides, DP obtained higher scores on the likability with no significant difference on the difficulty (p < 0.05, Wilcoxon signed test). The results showed that the drip drops were reliable acoustic materials as stimuli in an auditory BCI system.

Keywords

P300 Auditory BCI Drip drops Online accuracy User-friendness 

Notes

Acknowledgements

This work was supported in part by the Grant National Natural Science Foundation of China, under Grant Nos. 91420302, 61573142, and 61703407. This work was also supported by the programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Shanghai Chenguang Program under Grant 14CG31, and the Foundation of Key Laboratory of Science and Technology for National Defense (No. 6142222030301).

References

  1. Altmann CF, Bledowski C, Wibral M, Kaiser J (2007) Processing of location and pattern changes of natural sounds in the human auditory cortex. Neuroimage 35(3):1192–1200CrossRefPubMedGoogle Scholar
  2. Amenedo E, Dıaz F (1998) Aging-related changes in processing of non-target and target stimuli during an auditory oddball task. Biol Psychol 48(3):235–267CrossRefPubMedGoogle Scholar
  3. Başar E, Güntekin B, Atagün İ, Gölbaşı BT, Tülay E, Özerdem A (2012) Brain’s alpha activity is highly reduced in euthymic bipolar disorder patients. Cogn Neurodyn 6(1):11–20CrossRefPubMedGoogle Scholar
  4. Baykara E et al (2016) Effects of training and motivation on auditory P300 brain–computer interface performance. Clin Neurophysiol 127(1):379–387CrossRefPubMedGoogle Scholar
  5. Cummings A, Čeponienė R, Koyama A, Saygin AP, Townsend J, Dick F (2006) Auditory semantic networks for words and natural sounds. Brain Res 1115(1):92–107CrossRefPubMedGoogle Scholar
  6. Del Cul A, Baillet S, Dehaene S (2007) Brain dynamics underlying the nonlinear threshold for access to consciousness. PLoS biol 5(10):e260CrossRefPubMedPubMedCentralGoogle Scholar
  7. Donchin E, Spencer KM, Wijesinghe R (2000) The mental prosthesis: assessing the speed of 39 P300-based brain–computer interface. IEEE Trans Rehabilit Eng Publ IEEE Eng Med Biol Soc 8(2):174–179Google Scholar
  8. Farwell LA (2012) Brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials. Cogn Neurodyn 6(2):115CrossRefPubMedPubMedCentralGoogle Scholar
  9. Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70(6):510–523CrossRefPubMedGoogle Scholar
  10. Farwell LA, Richardson DC, Richardson GM (2013) Brain fingerprinting field studies comparing P300-MERMER and P300 brainwave responses in the detection of concealed information. Cogn Neurodyn 7(4):263CrossRefPubMedGoogle Scholar
  11. Furdea A, Halder S, Krusienski DJ, Bross D, Nijboer F, Birbaumer N, Kübler A (2009) An auditory oddball (P300) spelling system for brain–computer interfaces. Psychophysiology 46(3):617–625CrossRefPubMedGoogle Scholar
  12. Güntekin B, Başar E (2010) A new interpretation of P300 responses upon analysis of coherences. Cogn Neurodyn 4(2):107–118CrossRefPubMedPubMedCentralGoogle Scholar
  13. Halder S et al (2010) An auditory oddball brain-computer interface for binary choices. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 121(4):516–523CrossRefGoogle Scholar
  14. Halder S, Käthner I, Kübler A (2016) Training leads to increased auditory brain–computer interface performance of end-users with motor impairments. Clin Neurophysiol 127(2):1288–1296CrossRefPubMedGoogle Scholar
  15. Hill N, Schölkopf B (2012) An online brain–computer interface based on shifting attention to concurrent streams of auditory stimuli. J Neural Eng 9(2):026011CrossRefPubMedPubMedCentralGoogle Scholar
  16. Hill NJ, Lal TN, Bierig K, Birbaumer N (2005) Attention modulation of auditory event-related potentials in a brain-computer interface. In: IEEE international workshop on biomedical circuits and systems, pp S3/5/INV–S3/17–20Google Scholar
  17. Hoffmann U, Vesin JM, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167(1):115–125CrossRefPubMedGoogle Scholar
  18. Höhne J (2012) Natural stimuli improve auditory BCIs with respect to ergonomics and performance. J Neural Eng 9(4):2099–2102CrossRefGoogle Scholar
  19. Huang M, Daly I, Jin J, Zhang Y, Wang X, Cichocki A (2016) An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps. Cogn Neurodyn 10(3):1–9CrossRefGoogle Scholar
  20. Jeon JY, Lee PJ, You J, Kang J (2010) Perceptual assessment of quality of urban soundscapes with combined noise sources and water sounds. J Acoust Soc Am 127(3):1357–1366CrossRefPubMedGoogle Scholar
  21. Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang X, Neuper C (2011) An adaptive P300-based control system. J Neural Eng 8(3):292–301CrossRefGoogle Scholar
  22. Jin J, Allison BZ, Zhang Y, Wang X, Cichocki A (2014) An ERP-based BCI using an oddball paradigm with different faces and reduced errors in critical functions. Int J Neural Syst 24(8):1450027-1450027CrossRefGoogle Scholar
  23. Jin J, Sellers EW, Zhou S, Zhang Y, Wang X, Cichocki A (2015) A P300 brain–computer interface based on a modification of the mismatch negativity paradigm. Int J Neural Syst 25(3):595–599CrossRefGoogle Scholar
  24. Klobassa DS, Vaughan TP, Schwartz NE, Wolpaw JR, Neuper C, Sellers EW (2009) Toward a high-throughput auditory P300-based brain–computer interface. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 120(7):1252–1261CrossRefGoogle Scholar
  25. Kübler A, Furdea A, Halder S, Hammer EM, Nijboer F, Kotchoubey B (2009) A brain–computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Ann N Y Acad Sci 1157(1):90–100CrossRefPubMedGoogle Scholar
  26. Long J, Gu Z, Li Y, Yu T, Li F, Fu M (2011) Semi-supervised joint spatio-temporal feature selection for P300-based BCI speller. Cogn Neurodyn 5(4):387–398CrossRefPubMedPubMedCentralGoogle Scholar
  27. Lopez-Gordo MA, Fernandez E, Romero S, Pelayo F, Prieto A (2012) An auditory brain–computer interface evoked by natural speech. J Neural Eng 9(3):408–417CrossRefGoogle Scholar
  28. Lulé D, Noirhomme Q, Kleih SC (2013) Probing command following in patients with disorders of consciousness using a brain–computer interface. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 124(1):101–106CrossRefGoogle Scholar
  29. Martens S, Hill N, Farquhar J (2009) Overlap and refractory effects in a brain? computer interface speller based on the visual P300 event-related potential. J Neural Eng 6(2):026003CrossRefPubMedGoogle Scholar
  30. Martinez P, Bakardjian H, Cichocki A (2007) Fully online multicommand brain–computer interface with visual neurofeedback using SSVEP paradigm. Comput Intell Neurosci 2007:94561. https://doi.org/10.1155/2007/94561 CrossRefPubMedCentralGoogle Scholar
  31. Monica F, Demetrios K, Emanuel D (1995) P300 and recall in an incidental memory paradigm. Humana Press, New YorkGoogle Scholar
  32. Nijboer F, Furdea A, Gunst I, Mellinger J, Mcfarland DJ, Birbaumer N, Kübler A (2008) An auditory brain–computer interface (BCI). J Neurosci Methods 167(1):43–50CrossRefPubMedGoogle Scholar
  33. Pan J, Li Y, Gu Z, Yu Z (2013) A comparison study of two P300 speller paradigms for brain–computer interface. Cogn Neurodyn 7(6):523–529CrossRefPubMedPubMedCentralGoogle Scholar
  34. Park M et al (2016) Dysfunctional information processing during an auditory event-related potential task in individuals with Internet gaming disorder. Transl Psychiatry 6:e721. https://doi.org/10.1038/tp.2015.215 CrossRefPubMedPubMedCentralGoogle Scholar
  35. Puanhvuan D, Khemmachotikun S, Wechakarn P, Wijarn B, Wongsawat Y (2017) Navigation-synchronized multimodal control wheelchair from brain to alternative assistive technologies for persons with severe disabilities. Cogn Neurodyn 11(2):1–18CrossRefGoogle Scholar
  36. Schreuder M, Tangermann M, Blankertz B (2009) Initial results of a high-speed spatial auditory BCI. Int J Bioelectromagn 11(2):105–109Google Scholar
  37. Schreuder M, Blankertz B, Tangermann M (2010) A new auditory multi-class brain–computer interface paradigm: spatial hearing as an informative cue. PLoS ONE 5(3):e9813CrossRefPubMedPubMedCentralGoogle Scholar
  38. Schreuder M, Rost T, Tangermann M (2011) Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI. Front Neurosci 5:112. https://doi.org/10.3389/fnins.2011.00112 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Sellers EW, Donchin E (2006) A P300-based brain–computer interface: initial tests by ALS patients. Clin Neurophysiol 117(3):538–548CrossRefPubMedGoogle Scholar
  40. Simon N, Käthner I, Ruf CA, Pasqualotto E, Kübler A, Halder S (2015) An auditory multiclass brain–computer interface with natural stimuli: usability evaluation with healthy participants and a motor impaired end user. Front Hum Neurosci 8:1039. https://doi.org/10.3389/fnhum.2014.01039 CrossRefPubMedPubMedCentralGoogle Scholar
  41. Sutton S, Braren M, Zubin J, John ER (1965) Evoked-potential correlates of stimulus uncertainty. Science 150(3700):1187–1188CrossRefPubMedGoogle Scholar
  42. Tervaniemi M, Schröger E, Saher M, Näätänen R (2000) Effects of spectral complexity and sound duration on automatic complex-sound pitch processing in humans–a mismatch negativity study. Neurosci Lett 290(1):66–70CrossRefPubMedGoogle Scholar
  43. Theunissen FE, Elie JE (2014) Neural processing of natural sounds. Nat Rev Neurosci 15(6):355–366CrossRefPubMedGoogle Scholar
  44. Wang D, Chang P (2008) An oscillatory correlation model of auditory streaming. Cogn Neurodyn 2(1):7–19CrossRefPubMedPubMedCentralGoogle Scholar
  45. Xu M et al (2016) Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin. Neuroimage 134:204–212CrossRefPubMedGoogle Scholar
  46. Yin E, Zhou Z, Jiang J, Chen F, Liu Y, Hu D (2013) A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm. J Neural Eng 10(2):026012CrossRefPubMedGoogle Scholar
  47. Zhang Y, Guo D, Xu P, Zhang Y, Yao D (2016) Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index. Cogn Neurodyn 10(6):505–511CrossRefPubMedGoogle Scholar
  48. Zhou S, Allison BZ, Kübler A, Cichocki A, Wang X, Jin J (2016) Effects of background music on objective and subjective performance measures in an auditory BCI. Front Comput Neurosci 10:105. https://doi.org/10.3389/fncom.2016.00105 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Zhu D, Bieger J, Molina GG, Aarts RM (2010) A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci. https://doi.org/10.1155/2010/702357

Copyright information

© Springer Science+Business Media B.V. 2017

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.College of Mechatronics and AutomationNational University of Defense TechnologyChangshaPeople’s Republic of China

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