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

In: Information Systems and Neuroscience

Author

Listed:
  • Ricardo Buettner

    (Aalen University)

  • Thilo Rieg

    (Aalen University)

  • Janek Frick

    (Aalen University)

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.

Suggested Citation

  • Ricardo Buettner & Thilo Rieg & Janek Frick, 2020. "Machine Learning Based Diagnosis of Diseases Using the Unfolded EEG Spectra: Towards an Intelligent Software Sensor," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane Randolph & Thomas Fis (ed.), Information Systems and Neuroscience, pages 165-172, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-28144-1_18
    DOI: 10.1007/978-3-030-28144-1_18
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