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

Probabilities-at-year for the SSB to exceed the limit of recruitment overfishing .

<p>Alternative fishing management strategies, levels of seal reduction, and environmental conditions are considered. <b>(a)</b> Current environmental conditions (CIL = 0.25°C) for 30% (a–1) and 50% (a–2) seal reduction. <b>(b)</b> Warming environmental conditions (CIL = 0.75°C) for 30% (b–2) and 50% (b–3) seal reduction. Plain and dashed lines indicate the effects of reduced catch and cod fishery moratorium, respectively. Mean and 95% confidence intervals of the probabilities are displayed.</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

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

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

Cod total catches forecast in years 2010–2040 under water standard conditions.

<p>[<b>(a)</b>: CIL = 0.25°C] and warming conditions [<b>(b)</b>: CIL = 0.75°C], for the reduced catch fishing regime. Plain lines and grey areas indicate median values and 90%-confidence domains, respectively.</p

Deterministic and stochastic processes used in SIMCAB estimation and projection models, respectively. iid: independent and identically distributed; : distributed as. : binomial distribution with probability parameter .

<p>Deterministic and stochastic processes used in SIMCAB estimation and projection models, respectively. iid: independent and identically distributed; : distributed as. : binomial distribution with probability parameter .</p

Cod SSB forecast in years 2010–2040 under water warming conditions (cold intermediate layer (CIL) anomaly = 0.75°C).

<p>Plain lines and grey areas indicate median values and 90%-confidence domains, respectively. The dashed line indicate the limit of recruitment overfishing . The red dotted line indicates the complete recovery point B.</p

Parameters, variables and associated equations used in the SIMCAB estimation model. and index age and year, respectively. NoI: number of individuals; NoE: number of eggs; “Age of half-vulnerability” indicates the age at which 50% of the individuals are vulnerable to survey and commercial gears indexed by and , respectively.

<p>Parameters, variables and associated equations used in the SIMCAB estimation model. and index age and year, respectively. NoI: number of individuals; NoE: number of eggs; “Age of half-vulnerability” indicates the age at which 50% of the individuals are vulnerable to survey and commercial gears indexed by and , respectively.</p

Stochasticity in the input parameters for the SIMCAB projection model. The notation indicates the estimated abundance at age in 2009 from data between 1984 and 2009.

<p>Stochasticity in the input parameters for the SIMCAB projection model. The notation indicates the estimated abundance at age in 2009 from data between 1984 and 2009.</p

Observation equations for the SIMCAB estimation model. iid: independent and identically distributed; : distributed as; : normal distribution; <i>Dir</i>: Dirichlet distribution.

<p>Observation equations for the SIMCAB estimation model. iid: independent and identically distributed; : distributed as; : normal distribution; <i>Dir</i>: Dirichlet distribution.</p