Managing the Ethical Dimensions of Brain-Computer Interfaces in eHealth: An SDLC-based Approach

A growing range of brain-computer interface (BCI) technologies is being employed for purposes of therapy and human augmentation. While much thought has been given to the ethical implications of such technologies at the ‘macro’ level of social policy and ‘micro’ level of individual users, little attention has been given to the unique ethical issues that arise during the process of incorporating BCIs into eHealth ecosystems. In this text a conceptual framework is developed that enables the operators of eHealth ecosystems to manage the ethical components of such processes in a more comprehensive and systematic way than has previously been possible. The framework’s first axis defines five ethical dimensions that must be successfully addressed by eHealth ecosystems: 1) beneficence; 2) consent; 3) privacy; 4) equity; and 5) liability. The second axis describes five stages of the systems development life cycle (SDLC) process whereby new technology is incorporated into an eHealth ecosystem: 1) analysis and planning; 2) design, development, and acquisition; 3) integration and activation; 4) operation and maintenance; and 5) disposal. Known ethical issues relating to the deployment of BCIs are mapped onto this matrix in order to demonstrate how it can be employed by the managers of eHealth ecosystems as a tool for fulfilling ethical requirements established by regulatory standards or stakeholders’ expectations. Beyond its immediate application in the case of BCIs, we suggest that this framework may also be utilized beneficially when incorporating other innovative forms of information and communications technology (ICT) into eHealth ecosystems

Mean error and segmentation rates over 100 cross-validation datasets for correcting between 1 and 5 isolated “at sea” positions.

<p>Mean error and segmentation rates over 100 cross-validation datasets for correcting between 1 and 5 isolated “at sea” positions.</p

Smoothed mean densities of observed (as declared in logbooks, a) and predicted dFAD fishing sets (as derived from the corrected RF outputs, b) for the period 2007–2011.

<p>Densities were calculated on a 1° grid and smoothed using the two dimensional density estimation function <i>kde2d</i> of the MASS package in R (bandwidth chosen according to the rule-of-thumb provided in the function <i>bandwith</i>.<i>nrd</i>).</p

Location of raw GPS buoy positions in the Atlantic (a) and Indian (b) Oceans from January 2007 to December 2011.

<p>Location of raw GPS buoy positions in the Atlantic (a) and Indian (b) Oceans from January 2007 to December 2011.</p

Example of vessel (blue line) and buoy (red line) trajectories inferred from VMS and buoy GPS positions, respectively.

<p>After leaving the port of Abidjan (black square) the boat heads to the East in the direction of the Gulf of Guinea, before heading to the West in the direction of Dakar and conducting a series of fishing sets (grey dots). The overlap of the buoy and vessel trajectories indicates that the vessel turned on this particular buoy (1) before entering the port of Dakar. The buoy was likely deployed after leaving the port, shortly after performing a fishing set (2).</p

Northern Gulf of Saint Lawrence (NAFO divisions 4R and 4S).

<p>Northern Gulf of Saint Lawrence (NAFO divisions 4R and 4S).</p

Time (a) and distance (b) at sea per ocean (in d and km) as a function of recapture month.

<p>Time (a) and distance (b) at sea per ocean (in d and km) as a function of recapture month.</p

List of predictor variables considered in the classification models.

<p>t-1, t and t+1 represent 3 consecutive positions of buoys over time.</p

Mean yearly dFAD density (a) and ineffective dFAD effort (b) for the period 2007–2011.

<p>Black areas correspond to 1° grid cells where at least one French or Spanish fishing set occurred over the period 2006–2012.</p

Process components, observation functions and associated equations in the SIMCAB model. Notations , , , index age, year, survey, and commercial, respectively. The term is the indicator function of event .

<p>Process components, observation functions and associated equations in the SIMCAB model. Notations , , , index age, year, survey, and commercial, respectively. The term is the indicator function of event .</p