Imitated Mind Uploading by Using Electroencephalography
Authors: Ryota Horie, Kenta Kaneko
Abstract: In recent years, technology of brain-computer interface has been developed, and the technology has potential extensibility in combination with ubiquitous environments. In science fiction, an idea that personality is copied to a computational device by scanning brain activity, called mind uploading, ghost dubbing, and so on, has been frequently represented. If the idea becomes realized in a future ubiquitous world, design of highly human-friendly interfaces is expected. In this study, as a step towards realizing the idea, we proposed a method to imitate the mind uploading by using electroencephalography (EEG). We proposed a novel method to extract and digitize an essential feature of the EEG signals by using Hilbert-Huang transform (HHT) and symbolic dynamics analysis. A sequence of symbols was obtained from each of the EEG measurement. Then, we constructed 2nd-order Markov sources from the symbol sequences. Both of the cluster analysis and identification tests by human subjects revealed that the Markov source successfully represented both personal invariants and inter individual differences in EEG signals. In sum, we concluded that the imitated mind uploading can be realized by using EEG signals.
Keywords: Mind Uploading, Electroencephalography, Hilbert-Huang Transform, Symbolic Dynamics Analysis
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