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Neural Interfaces in Digital Product Design

Tom Page*, Gisli Thorsteinsson**

* Associate Professor, Department of Product Design, Nottingham Trent University, England.

** Professor, Department of Design and Craft Education, University of Iceland, Iceland.

Periodicity:January - March'2018
DOI : https://doi.org/10.26634/jdp.6.1.15155

Abstract

Brain Computer Interfaces (BCI's) allow people to control computers and other devices by thought alone (Bogue, 2010). The technology is beginning to migrate from research laboratories to real world applications, which raises a number of design issues. The aim of this research was to identify existing and possible future applications of this new technology and explore design issues related to their development. The literature review revealed conflicting evidence surrounding the capabilities of low cost, consumer BCIs. Manufacturers of these systems suggest the technology can measure the concentration level of users whilst performing mental tasks. This study aimed to determine how accurate this measure of concentration is through a controlled experiment. In the trial, 14 participants completed a range of mental exercises which gradually increased in difficulty. During the tasks users wore a BCI headset, which measured their level of attention. Users were asked to assess the level of the concentration required to complete the task using a five point rating system. Data recorded by the headset was compared to the subjective measures and no significant correlations were found. This indicates such devices cannot currently be used to accurately measure the concentration. The report identifies limitations in the current technology, which may contribute to this inaccuracy and suggests that the increased contribution from designers may help overcome these limitations.

Keywords

Brain Computer Interfaces, Product Design, Technology, BCI Systems

How to Cite this Article?

Page, T., and Thorsteinsson, G. (2018). Neural Interfaces in Digital Product Design. i-manager's Journal on Digital Signal Processing, 6(1), 1-9. https://doi.org/10.26634/jdp.6.1.15155

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