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Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Abstract
We present the first stage of our on-going artist-driven BCI collaboration, where we equipped an artist with the brain scanning technique functional Near Infrared Spectroscopy (fNIRS) in order to record mental workload levels during her creative practice. The artists are interested in exposing the hidden cognitive processes involved in their creative practice, in order to reuse or integrate the data into their performances. The researchers are interested in collecting unstructured ?in the wild? fNIRS data, and to see how the artists interpret the data retrospectively. We highlight some interesting early examples from the data and describe our on-going plans. We will have completed a second data collection before the workshop.
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Solovey, E. T., Girouard, A., Chauncey, K., Hirshfield, L. M., Sassaroli, A., Zheng, F., Fantini, S., & Jacob, R. J. K. (2009) · Proceedings of the 22nd annual ACM symposium on User interface software and technology · doi:10.1145/1622176.1622207 · Cited by 115
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Metadata
- Publisher
- ACM
- Published
- 2018-04-21
- OpenAlex
- W2801816216
- Cited by
- 569
- References
- 1
- Type
- paratext
- Language
- en
- Open access
- closed
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