HAMR@ISMIR 2015 Proceedings

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====== Brain Beats: Tempo Tracking in EEG Data ======

| Authors | Sebastian Stober, Thomas Prätzlich, Meinard Müller |
| Affiliation | University of Western Ontario; International Audio Laboratories Erlangen|
| Code | [[https://github.com/github/dmca|Github Link]] |
| Dependencies | [[https://github.com/sstober/openmiir| OpenMIIR Dataset (Github)]] |
|  | [[https://github.com/sstober/deepthought| deepthought project (Github)]] for data export |
|  | [[http://resources.mpi-inf.mpg.de/MIR/tempogramtoolbox/| Tempogram Toolbox]] |


===== Opening Question =====

Can we track the beat or the tempo of a music piece in brain waves recorded during listening? This is the question we tackled on the hack day at ISMIR 2015.

===== Data =====

The OpenMIIR dataset((Sebastian Stober, Avital Sternin, Adrian M. Owen and Jessica A. Grahn: "Towards Music Imagery Information Retrieval: Introducing the OpenMIIR Dataset of EEG Recordings from Music Perception and Imagination." In: Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR’15), 2015. [[ http://ismir2015.uma.es/articles/224_Paper.pdf |Paper]] [[http://bib.sebastianstober.de/ismir2015poster.pdf|Poster]])) comprises EEG recordings((Electroencephalography (EEG) is a popular non-invasive neuroimaging technique that relies on electrodes placed on the scalp to measure the electrical activity of the brain.)) of people listing to 12 short music pieces. 
The following figure shows the waveform of the acoustic stimulus (waveform of a music recording) and the EEG signal.

{{:brainbeats_wiki_figure_1.png?400|}}

===== Problem Specification =====

We wanted to know whether it is possible to track the tempo in the EEG signal just as this would be done for audio data. 

{{:brainbeats_wiki_figure_2.png?400|}}

The Tempogram Toolbox already does a great job for the music stimuli.

{{:brainbeats_wiki_figure_3.png?400|}}

Our idea is to apply similar techniques to the EEG data. However,
because the EEG is very noisy, we applied some pre-processing.
First, we aggregated several EEG channels into one signal.
Then, we applied a suitable high-pass filter and normalized 
the signal by subtracting a kind of moving average curve.

{{:brainbeats_wiki_figure_4.png?400|}}

The resulting signal is then used as a kind of novelty curve.

{{:brainbeats_wiki_figure_5.png?400|}}
HAMR@ISMIR 2015 Proceedings

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