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The advantage of using collaborative brain-computer interfaces in improving human response in visual target recognition tests was investigated. We used a public EEG dataset created from recordings made using a 32-channel EEG system by Delorme et al. (2004) to compare the classification accuracy using one, two, and three EEG signal sets from different subjects. Fourteen participants performed a go/no-go...
In this paper, we show that it is possible to use electroencephalography (EEG) and multi-brain computing with two humans to guide an Interactive Genetic Algorithm (IGA) system. We show that combining neural activity across two brains increases accuracy to guide evolutionary search more effectively. The IGA system involves a simple task of evolving a polygon shape to approximate the shape of a target...
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