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

  • Ricardo BuettnerEmail author
  • Thilo Rieg
  • Janek Frick
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 32)

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 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Aalen UniversityAalenGermany

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