Brainwave implanted reservoir computing

This work aims to build a reservoir computing system to recognize signals with the help of brainwaves as the input signals. The brainwave signals were acquired as the participants were listening to the signals. The human brain in this study can be regarded as the assistant neural networks or non-lin...

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Bibliographic Details
Published inAIP advances Vol. 14; no. 1; pp. 015253 - 015253-6
Main Authors Chen, Li-Yu, Chen, Yi-Chun, Huang, Jason C., Sok, Sophie, Armbruster, Vincent, Chen, Chii-Chang
Format Journal Article
LanguageEnglish
Published Melville American Institute of Physics 01.01.2024
AIP Publishing LLC
Subjects
Computation
Frequency ranges
Neural networks
Recognition
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Summary:This work aims to build a reservoir computing system to recognize signals with the help of brainwaves as the input signals. The brainwave signals were acquired as the participants were listening to the signals. The human brain in this study can be regarded as the assistant neural networks or non-linear activation function to improve the signal recognition. We showed that within the brainwave frequency ranges from 14 to 16, 20, 30, and 32 Hz, the mean squared errors of the input signal recognition were lower than those without brainwaves. This result has demonstrated that the reservoir computing system with the help of human responses can obtain more precise results.
ISSN:2158-3226
2158-3226
DOI:10.1063/5.0186854