Machine Learning Based Diagnosis of Diseases Using the Unfolded EEG Spectra: Towards an Intelligent Software Sensor

Ricardo Buettner (), Thilo Rieg and Janek Frick
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Ricardo Buettner: Aalen University
Thilo Rieg: Aalen University
Janek Frick: Aalen University

A chapter in Information Systems and Neuroscience, 2020, pp 165-172 from Springer

Abstract: Abstract In this research-in-progress work we sketch a roadmap for the development of a novel machine-learning-based EEG software sensor. In the first step we present the idea to unfold the EEG standard bandwidths in a more fine-graded equidistant 99-point spectrum to improve accuracy when diagnosing diseases. We use this novel pre-processing step prior to entering a Random Forests classifier. In the second step we evaluate the approach on alcoholism and epilepsy and demonstrate that the approach outperforms all benchmarks. The third step sketches a further improvement by replacing the hard-coded equidistant 99-point spectrum with a flexibly-grading spectrum. In the fourth step we combine the flexibly-grading EEG spectrum, the spatial locations of the EEG electrodes, and the EEG recording time to train an intelligent EEG software sensor using self-organizing feature mapping. Our work contributes to NeuroIS research by analyzing EEG as a bio-signal though a novel machine-learning approach.

Keywords: Electroencephalography; Random forests; Spectral analysis; Machine learning (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-28144-1_18

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DOI: 10.1007/978-3-030-28144-1_18

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