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Start Over Journal roadknight, chris and 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 Database OAIster Publisher springer nature
5 results

1. Improving understanding of EEG measurements using transparent machine learning models

Abstract

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.

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2. Improving understanding of EEG measurements using transparent machine learning models

Abstract

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.

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3. Improving understanding of EEG measurements using transparent machine learning models

Abstract

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.

Full Text
View/download PDF

4. Improving understanding of EEG measurements using transparent machine learning models

Abstract

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.

Full Text
View/download PDF

5. Improving understanding of EEG measurements using transparent machine learning models

Abstract

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.

Full Text
View/download PDF