6.8 Introduction to Biosensors

So far in Music Moves we have only looked at motion sensors here in the methods track. This week we will explore how it is possible to measure biosignals, that is activity within the body itself.

Sensors measuring biosignals are often also called physiological sensors. Most of these sensors share the same sensing principle, that of measuring electrical current in various parts of the body. But since the biosignals vary considerably in strength throughout the body, the sensors are optimised differently:

• Galvanic skin response (GSR) refers to changes in skin conductance, and is often used on the fingers or in the palm of the hand. The GSR signal is highly correlated with emotional changes, and such sensors have been used to some extent in music research (see next video) as well as in music performance. A challenge with such signals is that may not be entirely straightforward to interpret, and elements like sweat may become an issue when worn for longer periods of time.

• Electromyograms (EMG) are used to measure muscle activity, and are particularly effective on the arms of musicians to pick up information about hand and finger motion. A challenge with EMG is to place the sensor(s) in a way such that they pick up the muscle activity properly. Later in this activity you will see an example of how EMG sensors can be used for musical interaction.

• Electrocardiograms (EKG) measure the electrical pulses from the heart, and can be used to extract information about heart rate and heart rate variability. The latter has been shown to correlate with emotional state and has also been used in music research.

• Electroencephalograms (EEG) are used to measure electrical pulses from the brain, using either a few sensors placed on the forehead, or hats with numerous sensors included. Due to the weak brain signals, such sensors need to have strong amplifiers and are therefore also suspect to a lot of interference and noise. Nevertheless, such sensors have also been applied in both music analysis and performance.

EEG is in many ways the first "step" towards doing brain imaging, which has also become more popular in music research over the last years. We will not cover this in Music Moves, but interested learners may find some useful links in the references below.

References

• Campbell, I. G. (2009). EEG Recording and Analysis for Sleep Research. In J. N. Crawley, C. R. Gerfen, M. A. Rogawski, D. R. Sibley, P. Skolnick, & S. Wray (Eds.), Current Protocols in Neuroscience. Hoboken, NJ, USA: John Wiley & Sons, Inc.
• Craig, D. G. (2005). An Exploratory Study of Physiological Changes during ?Chills? Induced by Music. Musicae Scientiae, 9(2), 273?287.
• Eaton, J., Jin, W., & Miranda, E. (2014). The Space Between Us. A Live Performance with Musical Score Generated via Emotional Levels Measured in EEG of One Performer and an Audience Member. In Proceedings of the International Conference on New Interfaces for Musical Expression (pp. 593?596). London.
• Fan, Y.-Y., & Sciotto, M. (2013). BioSync: An Informed Participatory Interface for Audience Dynamics and Audiovisual Content Co-creation using Mobile PPG and EEG. In Proceedings of the International Conference on New Interfaces for Musical Expression (pp. 248?251). Daejeon, Republic of Korea: Graduate School of Culture Technology, KAIST.
• Nakra, T. M. (2000, February). Inside the Conductor?s Jacket: Analysis, Interpretation and Musical Synthesis of Expressive Gesture. Massachusetts Institute of Technology, Cambridge, Mass.
• Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of NeuroEngineering and Rehabilitation, 9(1), 21.
• P?rez, M. A. O., & Knapp, R. B. (2008). BioTools: A Biosignal Toolbox for Composers and Performers. In R. Kronland-Martinet, S. Ystad, & K. Jensen (Eds.), Computer Music Modeling and Retrieval. Sense of Sounds (pp. 441?452). Springer Berlin Heidelberg.
• Zimny, G. H., & Weidenfeller, E. W. (1963). Effects of music upon GSR and heart-rate. The American Journal of Psychology, 311?314.