Brainwave implanted reservoir computing

Li Yu Chen; Yi Chun Chen; Jason C. Huang; Sophie Sok; Vincent Armbruster; Chii Chang Chen

doi:10.1063/5.0186854

Brainwave implanted reservoir computing

Li Yu Chen^*, Yi Chun Chen^*, Jason C. Huang, Sophie Sok, Vincent Armbruster, Chii Chang Chen^*

^*Corresponding author for this work

Department of Biotechnology and Laboratory Science in Medicine

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Article number	015253
Journal	AIP Advances
Volume	14
Issue number	1
DOIs	https://doi.org/10.1063/5.0186854
State	Published - 1 Jan 2024

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10.1063/5.0186854

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title = "Brainwave implanted reservoir computing",

abstract = "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.",

author = "Chen, {Li Yu} and Chen, {Yi Chun} and Huang, {Jason C.} and Sophie Sok and Vincent Armbruster and Chen, {Chii Chang}",

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Brainwave implanted reservoir computing

Abstract

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