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Authors: Nick Merrill 1 ; Thomas Maillart 1 ; Benjamin Johnson 2 and John Chuang 1

Affiliations: 1 UC Berkeley, United States ; 2 Carnegie Mellon University, United States

Keyword(s): Bio-signal Processing, Signal Quantization, Logarithmic Binning, Calibration, Mobile Physiological Computing.

Related Ontology Subjects/Areas/Topics: Applications ; Biosignal Acquisition, Analysis and Processing ; Human-Computer Interaction ; Methodologies and Methods ; Pattern Recognition ; Physiological Computing in Mobile Devices ; Physiological Computing Systems ; Software Engineering

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 several formats:
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 - PhyCS; ISBN 978-989-758-085-7; ISSN 2184-321X, SciTePress, pages 44-51. DOI: 10.5220/0005238800440051

@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 - PhyCS},
year={2015},
pages={44-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005238800440051},
isbn={978-989-758-085-7},
issn={2184-321X},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Physiological Computing Systems - PhyCS
TI - Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique
SN - 978-989-758-085-7
IS - 2184-321X
AU - Merrill, N.
AU - Maillart, T.
AU - Johnson, B.
AU - Chuang, J.
PY - 2015
SP - 44
EP - 51
DO - 10.5220/0005238800440051
PB - SciTePress

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