Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique

Nick Merrill, Thomas Maillart, Benjamin Johnson, John Chuang

Abstract

This paper exhibits two methods for decreasing the time associated with training a machine learning classifier on biometric signals. Using electroencephalography (EEG) data obtained from a consumer-grade headset with a single electrode, we show that these methods produce significant gains in the computational performance and calibration time of a simple brain-computer interface (BCI) without significantly decreasing accuracy. We discuss the relevance of reduced feature vector size to the design of physiological computing applications.

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Paper Citation


in Harvard Style

Merrill N., Maillart T., Johnson B. and Chuang J. (2015). Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique . In Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-085-7, pages 44-51. DOI: 10.5220/0005238800440051


in Bibtex Style

@conference{phycs15,
author={Nick Merrill and Thomas Maillart and Benjamin Johnson and John Chuang},
title={Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique},
booktitle={Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2015},
pages={44-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005238800440051},
isbn={978-989-758-085-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique
SN - 978-989-758-085-7
AU - Merrill N.
AU - Maillart T.
AU - Johnson B.
AU - Chuang J.
PY - 2015
SP - 44
EP - 51
DO - 10.5220/0005238800440051