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

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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.

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References

  • 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):1192–1200

    Article  PubMed  Google Scholar 

  • 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):235–267

    Article  CAS  PubMed  Google Scholar 

  • 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):11–20

    Article  PubMed  Google Scholar 

  • Baykara E et al (2016) Effects of training and motivation on auditory P300 brain–computer interface performance. Clin Neurophysiol 127(1):379–387

    Article  CAS  PubMed  Google Scholar 

  • 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):92–107

    Article  CAS  PubMed  Google Scholar 

  • Del Cul A, Baillet S, Dehaene S (2007) Brain dynamics underlying the nonlinear threshold for access to consciousness. PLoS biol 5(10):e260

    Article  PubMed  PubMed Central  Google Scholar 

  • 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):174–179

    CAS  Google Scholar 

  • Farwell LA (2012) Brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials. Cogn Neurodyn 6(2):115

    Article  PubMed  PubMed Central  Google Scholar 

  • 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):510–523

    Article  CAS  PubMed  Google Scholar 

  • 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):263

    Article  PubMed  Google Scholar 

  • 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):617–625

    Article  CAS  PubMed  Google Scholar 

  • Güntekin B, Başar E (2010) A new interpretation of P300 responses upon analysis of coherences. Cogn Neurodyn 4(2):107–118

    Article  PubMed  PubMed Central  Google Scholar 

  • Halder S et al (2010) An auditory oddball brain-computer interface for binary choices. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 121(4):516–523

    Article  CAS  Google Scholar 

  • 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):1288–1296

    Article  CAS  PubMed  Google Scholar 

  • 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):026011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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–S3/17–20

  • Hoffmann U, Vesin JM, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167(1):115–125

    Article  PubMed  Google Scholar 

  • Höhne J (2012) Natural stimuli improve auditory BCIs with respect to ergonomics and performance. J Neural Eng 9(4):2099–2102

    Article  Google Scholar 

  • 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):1–9

    Article  Google Scholar 

  • 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):1357–1366

    Article  PubMed  Google Scholar 

  • 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):292–301

    Article  Google Scholar 

  • 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):1450027-1450027

    Article  Google Scholar 

  • 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):595–599

    Article  Google Scholar 

  • 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):1252–1261

    Article  CAS  Google Scholar 

  • 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):90–100

    Article  PubMed  Google Scholar 

  • 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):387–398

    Article  PubMed  PubMed Central  Google Scholar 

  • 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):408–417

    Article  Google Scholar 

  • 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):101–106

    Article  Google Scholar 

  • 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):026003

    Article  CAS  PubMed  Google Scholar 

  • 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

    Article  PubMed Central  Google Scholar 

  • Monica F, Demetrios K, Emanuel D (1995) P300 and recall in an incidental memory paradigm. Humana Press, New York

    Google Scholar 

  • 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):43–50

    Article  PubMed  Google Scholar 

  • 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):523–529

    Article  PubMed  PubMed Central  Google Scholar 

  • 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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):1–18

    Article  Google Scholar 

  • Schreuder M, Tangermann M, Blankertz B (2009) Initial results of a high-speed spatial auditory BCI. Int J Bioelectromagn 11(2):105–109

    Google Scholar 

  • 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):e9813

    Article  PubMed  PubMed Central  Google Scholar 

  • 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

    Article  PubMed  PubMed Central  Google Scholar 

  • Sellers EW, Donchin E (2006) A P300-based brain–computer interface: initial tests by ALS patients. Clin Neurophysiol 117(3):538–548

    Article  PubMed  Google Scholar 

  • 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

    Article  PubMed  PubMed Central  Google Scholar 

  • Sutton S, Braren M, Zubin J, John ER (1965) Evoked-potential correlates of stimulus uncertainty. Science 150(3700):1187–1188

    Article  CAS  PubMed  Google Scholar 

  • 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):66–70

    Article  CAS  PubMed  Google Scholar 

  • Theunissen FE, Elie JE (2014) Neural processing of natural sounds. Nat Rev Neurosci 15(6):355–366

    Article  CAS  PubMed  Google Scholar 

  • Wang D, Chang P (2008) An oscillatory correlation model of auditory streaming. Cogn Neurodyn 2(1):7–19

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu M et al (2016) Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin. Neuroimage 134:204–212

    Article  PubMed  Google Scholar 

  • 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):026012

    Article  PubMed  Google Scholar 

  • 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):505–511

    Article  PubMed  PubMed Central  Google Scholar 

  • 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

    Article  PubMed  PubMed Central  Google Scholar 

  • 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

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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).

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Correspondence to Jing Jin or Xingyu Wang.

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Huang, M., Jin, J., Zhang, Y. et al. Usage of drip drops as stimuli in an auditory P300 BCI paradigm. Cogn Neurodyn 12, 85–94 (2018). https://doi.org/10.1007/s11571-017-9456-y

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