Improving Understanding of EEG Measurements Using Transparent Machine Learning Models

Physiological datasets such as Electroencephalography (EEG) data offer an insight into some of the less well understood aspects of human physiology. This paper investigates simple methods to develop models of high level behavior from low level electrode readings. These methods include using neuron a...

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Bibliographic Details
Published inHealth Information Science pp. 134 - 142
Main Authors Roadknight, Chris, Zong, Guanyu, Rattadilok, Prapa
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2019
SeriesLecture Notes in Computer Science
Subjects
CAPing
Deep Learning
Physiological data
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Summary:Physiological datasets such as Electroencephalography (EEG) data offer an insight into some of the less well understood aspects of human physiology. This paper investigates simple methods to develop models of high level behavior from low level electrode readings. These methods include using neuron activity based pruning and large time slices of the data. Both approaches lead to solutions whose performance and transparency are superior to existing methods.
ISBN:3030329615
9783030329617
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-32962-4_13