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Bigger data for big data: From Twitter to brain–computer interfaces

Published online by Cambridge University Press:  26 February 2014

Etienne B. Roesch ,
Frederic Stahl  and
Mohamed Medhat Gaber
Etienne B. Roesch
Affiliation:
School of Systems Engineering, University of Reading, Reading RG6 6AH, United Kingdom. [email protected]@reading.ac.ukhttp://etienneroes.chhttp://fredericstahl.wordpress.com Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading RG6 6AH, United Kingdom
Frederic Stahl
Affiliation:
School of Systems Engineering, University of Reading, Reading RG6 6AH, United Kingdom. [email protected]@reading.ac.ukhttp://etienneroes.chhttp://fredericstahl.wordpress.com
Mohamed Medhat Gaber
Affiliation:
School of Computing Science and Digital Media, The Robert Gordon University, Aberdeen AB25 1HG, United Kingdom. [email protected]://mohamedmgaber.weebly.com/
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Abstract

We are sympathetic with Bentley et al.’s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain–computer interfaces.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 37 , Issue 1 , February 2014 , pp. 97 - 98
Copyright
Copyright © Cambridge University Press 2014 

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