Using Physiological Signals to Evolve Art

Basa, Tristan; Go, Christian Anthony L.; Yoo, Kil-Sang; Lee, Won-Hyung

doi:10.1007/11732242_60

Cited by 5 publications

(13 citation statements)

References 8 publications

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“…For example, eye-tracking or a physiological measurement could be used. This is illustrated by the work of Basa et al (2006), who use measurements of EEG signals as the basis for a fitness function which distinguishes 'positive' emotional states, and then uses this to evolve art that provokes these states.…”

Section: Human Interactionmentioning

confidence: 99%

Fitness in evolutionary art and music: a taxonomy and future prospects

Johnson

2016

IJART

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This paper is concerned with the idea of fitness in art and music systems that are based on evolutionary computation. A taxonomy is presented of the ways in which fitness is used in such systems, with two dimensions: what the fitness function is applied to, and the basis by which the function is constructed. A large collection of papers are classified using this taxonomy. The paper then discusses a number of ideas that have not been used for fitness evaluation in evolutionary art and which might be valuable in future developments: memory, scaffolding, connotation and web search.

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Section: Human Interactionmentioning

confidence: 99%

Fitness in evolutionary art and music: a taxonomy and future prospects

Johnson

2016

IJART

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“…The authors report a 61% accuracy, but this might be a biased result. After three generations of the genetic algorithm (which takes around 36 minutes), subjects in [13] were asked to recall their actual choices -those picture that elicited more 'positive' feelings according to their conscious experience. Unfortunately, this approach is biased: the image selected by the classifier is used to generate variations that are then persistently shown to the subject, while the un-selected pictures are lost.…”

Section: Classification Accuracy Performance Drop and Comparison Wimentioning

confidence: 99%

Images from the mind: BCI image evolution based on rapid serial visual presentation of polygon primitives

Seoane

Gabler

Blankertz

2015

Brain-Computer Interfaces

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This paper provides a proof of concept for an EEGbased reconstruction of a visual image which is on a user's mind. Our approach is based on the Rapid Serial Visual Presentation (RSVP) of polygon primitives and Brain-Computer Interface (BCI) technology. The presentation of polygons that contribute to build a target image (because they match the shape and/or color of the target) trigger attention-related EEG patterns. Accordingly, these target primitives can be determined using BCI classification of EventRelated Potentials (ERPs). They are then accumulated in the display until a satisfactory reconstruction is reached. Selection steps have an average classification accuracy of 75%. 25% of the images could be reconstructed completely, while more than 65% of the available visual details could be captured on average. Most of the misclassifications were not misinterpretations of the BCI concerning users' intent; rather, users tried to select polygons that were different than what was intended by the experimenters. Open problems and alternatives to develop a practical BCI-based image reconstruction application are discussed.

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“…For example, eye-tracking or a physiological measurement could be used. This is illustrated by the work of Basa et al [1], who use measurements of EEG signals as the basis for a fitness function which distinguishes "positive" emotional states, and then uses this to evolve art that provokes these states.…”

Section: Fitness Basismentioning

confidence: 99%