Enhancing Wireless Non-invasive Brain-Computer Interfaces with an Encoder/Decoder Machine Learning Model Pair

Ernst R. Fanfan (), Joe Blankenship (), Sumit Chakravarty () and Adriane B. Randolph ()
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Ernst R. Fanfan: Kennesaw State University
Joe Blankenship: Kennesaw State University
Sumit Chakravarty: Kennesaw State University
Adriane B. Randolph: Kennesaw State University

A chapter in Information Systems and Neuroscience, 2022, pp 53-59 from Springer

Abstract: Abstract This project follows a design science research approach to demonstrate a proof-of-concept for developing a means to remove the wires from non-invasive, electroencephalographic brain-computer interface systems while maintaining data integrity and increasing the speed of transmission. This paper uses machine learning techniques to develop an encoder/decoder pair. The encoder learns the important information from the analog signal, reducing the amount of data encoded and transmitted. The decoder ignores the noise and expands the transmitted data for further processing. This paper uses one channel from a non-invasive BCI and organizes the analog signal in 500 datapoint frames. The encoder reduces the frames to seventy-five datapoints and after noise injection, the decoder successfully expands them back to virtually-indistinguishable frames from the originals. The hopes are for improved overall efficiency of non-invasive, wireless brain-computer interface systems and improved data collection for neuro-information systems.

Keywords: Electroencephalograph; Brain-computer interface; Machine learning; Data compression; NeuroIS (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-13064-9_5

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DOI: 10.1007/978-3-031-13064-9_5

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