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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Acknowledgements
This work is supported by Ningbo Municipal Bureau of Science and Technology (Grant No. 2017D10034).
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Roadknight, C., Zong, G., Rattadilok, P. (2019). Improving Understanding of EEG Measurements Using Transparent Machine Learning Models. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_13
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DOI: https://doi.org/10.1007/978-3-030-32962-4_13
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