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...
| Main Authors: | , , |
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| Format: | Book Section |
| Language: | English |
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Springer Nature
2019
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| Online Access: | https://eprints.nottingham.ac.uk/59612/ |
| _version_ | 1848799653182570496 |
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| author | Roadknight, Chris Zong, Guanyu Rattadilok, Prapa |
| author_facet | Roadknight, Chris Zong, Guanyu Rattadilok, Prapa |
| author_sort | Roadknight, Chris |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:39:05Z |
| format | Book Section |
| id | nottingham-59612 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:39:05Z |
| publishDate | 2019 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-596122020-01-09T03:59:25Z https://eprints.nottingham.ac.uk/59612/ Improving understanding of EEG measurements using transparent machine learning models Roadknight, Chris Zong, Guanyu Rattadilok, Prapa 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. Springer Nature 2019-10-08 Book Section PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/59612/1/Improving.pdf Roadknight, Chris, Zong, Guanyu and Rattadilok, Prapa (2019) Improving understanding of EEG measurements using transparent machine learning models. In: Health Information Science. Lecture Notes in Computer Science book series, 11837 . Springer Nature, Switzerland, pp. 134-142. ISBN 9783030329617 Deep Learning; Physiological data; CAPing http://dx.doi.org/10.1007/978-3-030-32962-4_13 doi:10.1007/978-3-030-32962-4_13 doi:10.1007/978-3-030-32962-4_13 |
| spellingShingle | Deep Learning; Physiological data; CAPing Roadknight, Chris Zong, Guanyu Rattadilok, Prapa Improving understanding of EEG measurements using transparent machine learning models |
| title | Improving understanding of EEG measurements using transparent machine learning models |
| title_full | Improving understanding of EEG measurements using transparent machine learning models |
| title_fullStr | Improving understanding of EEG measurements using transparent machine learning models |
| title_full_unstemmed | Improving understanding of EEG measurements using transparent machine learning models |
| title_short | Improving understanding of EEG measurements using transparent machine learning models |
| title_sort | improving understanding of eeg measurements using transparent machine learning models |
| topic | Deep Learning; Physiological data; CAPing |
| url | https://eprints.nottingham.ac.uk/59612/ https://eprints.nottingham.ac.uk/59612/ https://eprints.nottingham.ac.uk/59612/ |