EEuGene: employing electroencephalograph signals in the rating strategy of a hardware-based interactive genetic algorithm

We describe a novel interface and development platform for an interactive Genetic Algorithm (iGA) that uses Electroencephalograph (EEG) signals as an indication of fitness for selection for successive generations. A gaming headset was used to generate EEG readings corresponding to attention and meditation states from a single electrode. These were communicated via Bluetooth to an embedded iGA implemented on the Arduino platform. The readings were taken to measure subjects’ responses to predetermined short sequences of synthesised sound, although the technique could be applied any appropriate problem domain. The prototype provided sufficient evidence to indicate that use of the technology in this context is viable. However, the approach taken was limited by the technical characteristics of the equipment used and only provides proof of concept at this stage. We discuss some of the limitations of using biofeedback systems and suggest possible improvements that might be made with more sophisticated EEG sensors and other biofeedback mechanisms.

This is a post-peer-review, pre-copyedit version of an paper published in Research and Development in Intelligent Systems XXXIII: Incorporating Applications and Innovations in Intelligent Systems XXIV, Part XII. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-47175-4_25

Conference paper published as a Chapter in: Research and Development in Intelligent Systems XXXIII, pp 343-353.
Citation: "James-Reynolds C., Currie E. (2016) EEuGene: Employing Electroencephalograph Signals in the Rating Strategy of a Hardware-Based Interactive Genetic Algorithm. In: Bramer M., Petridis M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham"