Investigating music tempo as a feedback mechanism for closed-loop BCI control
Daly, I., Williams, D., Hwang, F., Kirke, A., Malik, A., Roesch, E., Weaver, J., Miranda, E. and Nasuto, S. (2014) Investigating music tempo as a feedback mechanism for closed-loop BCI control. Brain-Computer Interfaces, 1 (3-4). pp. 158-169. ISSN 2326-263X
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To link to this item DOI: 10.1080/2326263X.2014.979728
The feedback mechanism used in a brain-computer interface (BCI) forms an integral part of the closed-loop learning process required for successful operation of a BCI. However, ultimate success of the BCI may be dependent upon the modality of the feedback used. This study explores the use of music tempo as a feedback mechanism in BCI and compares it to the more commonly used visual feedback mechanism. Three different feedback modalities are compared for a kinaesthetic motor imagery BCI: visual, auditory via music tempo, and a combined visual and auditory feedback modality. Visual feedback is provided via the position, on the y-axis, of a moving ball. In the music feedback condition, the tempo of a piece of continuously generated music is dynamically adjusted via a novel music-generation method. All the feedback mechanisms allowed users to learn to control the BCI. However, users were not able to maintain as stable control with the music tempo feedback condition as they could in the visual feedback and combined conditions. Additionally, the combined condition exhibited significantly less inter-user variability, suggesting that multi-modal feedback may lead to more robust results. Finally, common spatial patterns are used to identify participant-specific spatial filters for each of the feedback modalities. The mean optimal spatial filter obtained for the music feedback condition is observed to be more diffuse and weaker than the mean spatial filters obtained for the visual and combined feedback conditions.
 Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002;113:767–791.  Mason SG, Birch GE. A general framework for braincomputer interface design. IEEE Trans Neural Syst Rehabil Eng. 2003;11:70–85.  Alimardani M, Nishio S, Ishiguro H. Effect of biased feedback on motor imagery learning in BCI-teleoperation system. Front Syst Neurosci. 2014;8:52.  Hwang HJ, Kim S, Choi S, Im CH. EEG-based braincomputer interfaces (BCIs): a thorough literature survey. Int J Hum Comput Interact. 2013;29:814–826.  Chin ZY, Ang KK, Wang C, Guan C. Online performance evaluation of motor imagery BCI with augmented-reality virtual hand feedback. Conf Proc IEEE Eng Med Biol Soc. 2010;3341–3344.  Hwang HJ, Kwon K, Im CH. Neurofeedback-based motor imagery training for brain-computer interface (BCI). J Neurosci Methods. 2009;179:150–156.  Kaiser V, Daly I, Pichiorri F, Mattia D, Müller-Putz GR, Neuper C. On the relationship between electrical brain responses to motor imagery and motor impairment in stroke. Stroke. 2012;43:2735–2740.  Prasad G, Herman P, Coyle D, McDonough S, Crosbie J. Using motor imagery based brain-computer interface for post-stroke rehabilitation. Conf Proc IEEE Eng Med Biol Soc. 2009;258–262.  Sitaram R, Caria A, Veit R, Gaber T, Rota G, Kuebler A, Birbaumer N. FMRI brain-computer interface: a tool for neuroscientific research and treatment. Comput Intell Neurosci. 2007;25487.  Birbaumer N, Ramos Murguialday A, Weber C, Montoya P. Neurofeedback and brain-computer interface clinical applications. Int Rev Neurobiol. 2009;86:107–117.  Ang KK, Guan C, Chua KSG, Ang BT, Kuah C, Wang C, et al. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. Conf Proc IEEE Eng Med Biol Soc. 2010;5549–5552.  Ramos-Murguialday A, Schürholz M, Caggiano V, Wildgruber M, Caria A, Hammer EM, Halder S, Birbaumer N. Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses. PLoS One. 2012;7:e47048.  Riccio A, Mattia D, Simione L, Olivetti M, Cincotti F. Eye-gaze independent EEG-based brain-computer interfaces for communication. J Neural Eng. 2012;9:045001.  Russman BS, Kazi KH. Spinal epidural hematoma and the Brown-Sequard syndrome. Neurology. 1971;21:1066–1068.  Smith DL, Akhtar AJ, Garraway WM. Proprioception and spatial neglect after stroke. Age Ageing. 1983;12:63–69.  McCreadie KA, Coyle DH, Prasad G. Sensorimotor learning with stereo auditory feedback for a brain-computer interface. Med Biol Eng Comput. 2013;51:285–293.  Higashi H, Rutkowski TM, Washizawa Y, Cichocki A, Tanaka T. EEG auditory steady state responses classification for the novel BCI. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011. p. 4576–4579.  Nakamura T, Namba H, Matsumoto T. Classification of auditory steady-state responses to speech data. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER); 2013. p. 1025–1028.  Schreuder M, Blankertz B, Tangermann M. A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS One. 2010;5:e9813.  Miranda ER. Brain-computer music interface for composition and performance. Int J Disabil Human Dev. 2006;5:119–126.  Münßinger JI, Halder S, Kleih SC, Furdea A, Raco V, Hösle A, Kübler A. Brain painting: first evaluation of a new braincomputer interface application with ALS-patients and healthy volunteers. Front Neurosci. 2010;4:182.  Miranda ER, Magee WL, Wilson JJ, Eaton J, Palaniappan R. Brain-computer music interfacing (BCMI): from basic research to the real world of special needs. Music Med. 2011;3:134–140.  Nijboer F, Furdea A, Gunst I, Mellinger J, McFarland DJ, Birbaumer N, Kübler A. An auditory brain-computer interface (BCI). J Neurosci Methods. 2008;167: 43–50.  Huron DB. Sweet anticipation: music and the psychology of expectation. Cambridge (MA): MIT Press; 2006. p. 462.  Kellaris JJ, Mantel SP, Altsech MB. Decibels, disposition, and duration: the impact of musical loudness and internal states on time perceptions. Adv Consum Res. 1996;23: 498–503.  Brodersen KH, Ong CS, Stephan KE, Buhmann JM. The balanced accuracy and its posterior distribution. In: 20th International Conference on Pattern Recognition; 2010. p. 3121–3124.  Daly I, Hallowell J, Hwang F, Kirke A, Malik A, Roesch E, Weaver J, Williams D, Miranda M, Nasuto S. Changes in music tempo entrain movement related brain activity. In: Proceedings of the EMBC; 2014.  Hammer EM, Halder S, Blankertz B, Sannelli C, Dickhaus T, Kleih S, Müller KR, Kübler A. Psychological predictors of SMR-BCI performance. Biol Psychol. 2012;89:80–86.  Kübler A, Neumann N, Kaiser J, Kotchoubey B, Hinterberger T, Birbaumer NP. Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. Arch Phys Med Rehabil. 2001;82: 1533–1539.  Makeig S, Leslie G, Mullen T, Sarma D, Bigdely-Shamlo N, Kothe C. First demonstration of a musical emotion BCI. Affect Comput Int Interact Lect Notes Comp Sci. 2011;6975:487–496.  Folgieri R, Zichella M. A BCI-based application in music. Comput Entertain. 2012;10:1–10.  Husain G, Thompson WF, Schellenberg EG. Effects of musical tempo and mode on arousal, mood, and spatial abilities. Music Percept. 2002;20:151–171.  Daly I, Malik A, Hwang F, Roesch E, Weaver J, Kirke A, Williams D, Miranda E, Nasuto SJ. Neural correlates of emotional responses to music: an EEG study. Neurosci Lett. 2014;573:52–57.