Blokland, Yvonne

Desain, Peter

Farquhar, Jason

Schaefer, Rebecca

English

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     Edinburgh Research Explorer                                      Single trial classification of perceived and imagined music fromEEGCitation for published version:Schaefer, R, Blokland, Y, Farquhar, J & Desain, P 2009, 'Single trial classification of perceived andimagined music from EEG'. in Proceedings of the 2009 Berlin BCI Workshop. Berlin BCI Workshop 2009,Berlin, Germany, 8-10 July.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Preprint (usually an early version)Published In:Proceedings of the 2009 Berlin BCI WorkshopPublisher Rights Statement:© Schaefer, R., Blokland, Y., Farquhar, J., & Desain, P. (2009). Single trial classification of perceived andimagined music from EEG. In Proceedings of the 2009 Berlin BCI Workshop.General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 20. Feb. 2015Single trial classiﬁcation of perceived and imagined music fromEEGRebecca S. Schaefer†, Yvonne Blokland‡, Jason Farquhar†& Peter Desain††Donders Institute for Brain, Cognition & Behav-ior: Centre for Cognition, Radboud University Nij-megen, The Netherlands‡Cognitive Artiﬁcial Intelligence, Utrecht Univer-sity, The NetherlandsSummaryWe have carried out two experiments to investi-gate imagined music as a possible paradigm fora brain-computer interface (BCI). In a ﬁrst ex-periment, we found that although all participantsshowed above-chance classiﬁcation for heard musi-cal phrases, only three out of six showed classiﬁableimagery. We used a sequence method to use multi-ple trials, which increased the rates for these par-ticipants and led to rates above 80% (in a 4-classproblem) for the best participant. To investigatemultitrial results in a continuous sequence, a sec-ond experiment was carried out. This time, actu-ally perceived music was detectable up to 98% cor-rect, but for imagery again the results were varied.The sequence method for the continuous epochsdid increase the rates, but not more so than forthe alternating stimulus sequences. Imagined mu-sic is shown to be an eﬀective BCI task, at leastfor a subset of users. As development is still in anearly stage, more work is necessary to ﬁne-tune theparadigm.BackgroundIn searching for a mental task suitable to drive aBCI, several types of imagery have been explored.After encouraging results for imagined rhythm [2].we wanted to further investigate musical stimuli.Music imagery has previously been reported as atask for BCI [1]; however in this case it was con-trasted with completely dissimilar tasks. Here, weinvestigate the possibilities of distinguishing be-tween diﬀerent imagined musical phrases. We usedself-selected musical phrases that were well knownto the participants, on the supposition that notonly does this reduce the memory load and exertionas compared to more eﬀortful tasks, but it wouldalso evoke a stronger brain response as the partic-ipants selected their own personal choice of musicthey like and respond to.Figure 1: A schematic overview of the stimulus se-quences used in experiment 1Experiment 1: EEG was measured in 6 parti-cipants, listening and imagining 4 diﬀerent musicalphrases, by alternating perception and imagery (seeFig. 1) and thus keeping the timelock. One secondof data was taken from each musical phrase andclassiﬁed per task (perception/imagery).Figure 2: A schematic overview of the stimulus se-cuences used in experiment 2Experiment 2: The current sequences did notallow for concatenation with continuous data, andadditionally were not suitable for a real-time im-plementation. Thus, in a subsequent experiment,the alternating sequences were replaced by only im-agery, with a metronome to give the time-lock and1two perception trials as instruction and the mu-sic time-stretched to yield equal-length phrases (seeFig 2).MethodFor Experiment 1, 1s-epochs were taken (musi-cal phrases had diﬀerent lengths) with a 200 mstimelag to prevent carryover from the perceptionepochs just before. For Experiment 2 the wholesegment was used. After some preprocessing steps,the multi-class problem was split into binary sub-problems and a linear logistic-regression classiﬁertrained and applied. The multi-class results are anaverage of these binary rates. To look at the eﬀectof using multiple trials, a 'maximum-likelihood' de-coding technique was used to combine per-epochclassiﬁer predictions to both generate a multi-classprediction and to generate sequence predictions.ResultsFigure 3 shows the results for Experiment 1. Itshows that increasing the number of epochs usedraises the rates for all participants except one (S4).Perception signals are clearly stronger than im-agery and go up to 90% and above for the besttwo participants after two seconds of data.0.2 0.3 0.4 0.5 0.6 0.7 1 2 3 4 5 6 7 8 9 10 Imagery S1 S2 S3 S4 S5 S6 0.2 0.4 0.6 0.8 1 1 2 3 4 5 6 7 8 9 10 Perception S1 S2 S3 S4 S5 S6 Figure 3: Classiﬁcation rates for Experiment one,using an accumulating number of 1s- epochs up toten. The colored lines indicate the subjects, and asthis is a 4-class problem chance level is at 0.25.Figure 4 shows the results for Experiment 2, withimagery results showing the best binary pair, andperception showing the results of a 5-class problem(as most binary problems went up to 100% fairlyquickly). It shows that again, increasing the num-ber of used epochs generally raises the rates for allparticipants, although it is not a straightforwardincrease as the stimuli were self-selected and epochlength thus varied.0.5 0.6 0.7 0.8 0.9 1 1 3 5 7 9 11 13 Imagery S7 S8 S9 S10 0.2 0.4 0.6 0.8 1 1 3 5 7 9 11 13 Perception S7 S8 S9 S11 Figure 4: Classiﬁcation rates for experiment two,using an accumulating number of epochs. The col-ored lines indicate the subjects, and the left graphshows the best binary pair in imagery (chance levelat 0.5) and the right shows the 5-class results fordetecting perceived music (chance level at 0.2).DiscussionGenerally, participants in Experiment 2 showed abetter performance, and this may be due to thechanged stimulus sequences, but this may also becaused by interpersonal diﬀerences. Even so, asthe results are preliminary, more work in this di-rection is needed to make more solid conclusions.We are currently developing this paradigm for areal-time setting, cutting oﬀ the sequence once agood rate is reached with a reasonable probabil-ity. The reported classiﬁcations indicate that notmuch is gained after 5 seconds of data, but this maychange in an online setting.Imagined music is shown to be a promisingparadigm for BCI, but also has to tackle problemsof illiteracy. However, for participants that showuseful brain signals, the performance is compara-ble to other popular BCI tasks. Future work willshow the feasibility in a real-time set-up.References[1] E. Curran, P. Sykacek, M. Stokes, S.J. Roberts,W. Penny, I. Johnsrude, and A.M. Owen. Cog-nitive tasks for driving a brain-computer inter-facing system: A pilot study. IEEE Transac-tions on Neural Systems and Rehabilitation En-gineering, 12(1):4854, 2004.[2] P. Desain. Bayesian detection of perceived andimagined rhythm from ERP. In Proceedings ofthe 18th International Congress on Acoustics,Kyoto, Japan, 2004.2

Single trial classification of perceived and imagined music from EEG

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